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software_name_mentions0 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Computer software for health care CQI .
The transition from quality assurance to continuous quality improvement ( CQI ) demands a basic change from centralized to decentralized information management .
In CQI , front - line staff can use personal computer ( PC ) software skills to manage , analyze , and act on process - related information .
Information services departments play a key role in this transition by , among other things , selecting software that optimally supports the PC - based work carried out by CQI teams .
A review of typical CQI team software requirements and of available PC software suggests that Microsoft Excel 4.0 is a nearly ideal software platform to support CQI teams .
OUTPUT:
| 10131011.txt |
software_name_mentions1 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Implementing a DRG - driven acuity system for nurse staffing under prospective hospital payment .
The " DRG - Nursing Unit Manager " is a multifunctional microcomputer software product designed for the effective management of nursing under a DRG prospective payment environment .
The system ' s signature is its simplicity and versatility , and will prove to be a significant management tool for the nursing department .
Written to execute on the IBM - PC - XT microcomputer , the nurse can start an application , enter patient demographics , assign Major Diagnostic Category ( MDC ) and Diagnosis Related Group ( DRG ) numbers , or make report selections .
With a minimal amount of keyboard usage the system collects and saves key data fields , retrieves nursing measures and length of stay norms , and produces reports on a demand basis .
The primary report produced by the system is a patient census listing with expected nursing intensities per patient for the next shift which is converted to a staffing projection for the upcoming three shifts for the existing census .
The system also allows the costing out of nursing services and produces charge - equivalent statistics so that the nursing station can function as a revenue - producing center .
Such an approach permits the identification of DRGs which are " winners " or " losers " under routine care costs .
OUTPUT:
| 10311121.txt |
software_name_mentions2 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Authoring software for courses delivered on the Web , Part 4 : Dreamweaver / CourseBuilder .
OUTPUT:
| 11299558.txt |
software_name_mentions3 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Combining speech recognition software with Digital Imaging and Communications in Medicine ( DICOM ) workstation software on a Microsoft Windows platform .
This presentation describes our experience in combining speech recognition software , clinical review software , and other software products on a single computer .
Different processor speeds , random access memory ( RAM ) , and computer costs were evaluated .
We found that combining continuous speech recognition software with Digital Imaging and Communications in Medicine ( DICOM ) workstation software on the same platform is feasible and can lead to substantial savings of hardware cost .
This combination optimizes use of limited workspace and can improve radiology workflow .
OUTPUT:
| 11442089.txt |
software_name_mentions4 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: A guide to using performance measurement systems for continuous improvement .
The Joint Commission on Accreditation of Healthcare Organizations requires accredited organizations to use a performance measurement system that meets its inclusion requirements to satisfy performance outcome and measurement expectations .
The system , known as the ORYX initiative , is used for both internal performance control and external performance comparisons .
This article outlines a three - step approach to using a performance measurement system based on the philosophy of continuous improvement and the methods of statistical process control ( SPC ) . SPC , the methodology recommended by the Joint Commission , can be applied to the analysis of many quality measures and can be implemented with Microsoft Excel software .
OUTPUT:
| 11482234.txt |
software_name_mentions5 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: An integrated software suite for surface - based analyses of cerebral cortex .
The authors describe and illustrate an integrated trio of software programs for carrying out surface - based analyses of cerebral cortex .
The first component of this trio , SureFit ( Surface Reconstruction by Filtering and Intensity Transformations ) , is used primarily for cortical segmentation , volume visualization , surface generation , and the mapping of functional neuroimaging data onto surfaces .
The second component , Caret ( Computerized Anatomical Reconstruction and Editing Tool Kit ) , provides a wide range of surface visualization and analysis options as well as capabilities for surface flattening , surface - based deformation , and other surface manipulations .
The third component , SuMS ( Surface Management System ) , is a database and associated user interface for surface - related data .
It provides for efficient insertion , searching , and extraction of surface and volume data from the database .
OUTPUT:
| 11522765.txt |
software_name_mentions6 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Evaluation of protein multiple alignments by SAM - T99 using the BAliBASE multiple alignment test set .
MOTIVATION : SAM - T99 is an iterative hidden Markov model - based method for finding proteins similar to a single target sequence and aligning them .
One of its main uses is to produce multiple alignments of homologs of the target sequence .
Previous tests of SAM - T99 and its predecessors have concentrated on the quality of the searches performed , not on the quality of the multiple alignment .
In this paper we report on tests of multiple alignment quality , comparing SAM - T99 to the standard multiple aligner , CLUSTALW .
RESULTS : The paper evaluates the multiple - alignment aspect of the SAM - T99 protocol , using the BAliBASE benchmark alignment database .
On these benchmarks , SAM - T99 is comparable in accuracy with ClustalW .
AVAILABILITY : The SAM - T99 protocol can be run on the web at http : / / www.cse.ucsc.edu / research / compbio / HMM - apps / T99 - query.html and the alignment tune - up option described here can be run at http : / / www.cse.ucsc.edu / research / compbio / HMM - apps / T99 - tuneup.html .
The protocol is also part of the standard SAM suite of tools .
http : / / www.cse.ucsc.edu / research / compbio / sam /
OUTPUT:
| 11524372.txt |
software_name_mentions7 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Genotype transposer : automated genotype manipulation for linkage disequilibrium analysis .
UNLABELLED : The purpose of this work is to provide the modern molecular geneticist with tools to perform more efficient and more accurate analysis of the genotype data they produce .
By using Microsoft Excel macros written in Visual Basic , we can translate genotype data into a form readable by the versatile software ' Arlequin ' , read the Arlequin output , calculate statistics of linkage disequilibrium , and put the results in a format for viewing with the software ' GOLD ' . AVAILABILITY : The software is available by FTP at : ftp : / / xcsg.iarc.fr / cox / Genotype _ Transposer / . SUPPLEMENTARY INFORMATION : Detailed instruction and examples are available at : ftp : / / xcsg.iarc.fr / cox / Genotype & _ Transposer / . Arlequin is available at : http : / / lgb.unige.ch / arlequin / . GOLD is available at : http : / / www.well.ox.ac.uk / asthma / GOLD / .
OUTPUT:
| 11524375.txt |
software_name_mentions8 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: A comparison of the Celera and Ensembl predicted gene sets reveals little overlap in novel genes .
OUTPUT:
| 11534548.txt |
software_name_mentions9 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Surgical simulation software for insertion of pedicle screws .
OBJECTIVE : As the first step toward finding noninvasive alternatives to the traditional methods of surgical training , we have developed a small , stand - alone computer program that simulates insertion of pedicle screws in different spinal vertebrae ( T10 - L5 ) . METHODS : We used Delphi 5.0 and DirectX 7.0 extension for Microsoft Windows .
This is a stand - alone and portable program .
RESULTS : The program can run on most personal computers .
It provides the trainee with visual feedback during practice of the technique .
At present , it uses predefined three - dimensional images of the vertebrae , but we are attempting to adapt the program to three - dimensional objects based on real computed tomographic scans of the patients .
The program can be downloaded at no cost from the web site : www.tums.ac.ir / downloads CONCLUSION : As a preliminary work , it requires further development , particularly toward better visual , auditory , and even proprioceptive feedback and use of the individual patient ' s data .
OUTPUT:
| 11844256.txt |
software_name_mentions10 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: D - ASSIRC : distributed program for finding sequence similarities in genomes .
MOTIVATION : Locating the regions of similarity in a genome requires the availability of appropriate tools such as ' Accelerated Search for SImilar Regions in Chromosomes ' ( ASSIRC ; Vincens et al. , Bioinformatics , 14 , 715 - 725 , 1998 ) . The aim of this paper is to present different strategies for improving this program by distributing the operations and data to multiple processing units and to assess the efficiency of the different implementations in terms of running time as a function of the number of processing units .
RESULTS : The new version D - ASSIRCis based on three alternative strategies of task sharing : ( 1 ) a distributed search using the splitting of studied sequences into large overlapping subsequences ( strategy ASS ) ; ( 2 ) two distributed searches for repeated exact motifs of fixed size either managed by a central processor ( strategy AGD ) or locally managed by numerous processors ( strategy ALD ) . The result is that the strategy ASSis suitable for a large number of processing units ( the time was divided by a factor of 12 when the number of processing units was increased from 1 to 16 ) wheras the strategy ALDis better for a small set of processors ( typically for four or six ) . The different proposed strategies are efficient for various applications in genomic research , particularly for locating similarities of nucleic sequences in large genomes .
AVAILABILITY : D - ASSIRCis freely available by anonymous FTP at ftp : / / ftp.ens.fr / pub / molbio / dassirc.tar.gz .
Sources and binaries for Solaris and Linux are included in the distribution .
OUTPUT:
| 11934744.txt |
software_name_mentions11 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Modelling cellular processes with Python and Scipy .
This paper shows how Python and Scipy can be used to simulate the time - dependent and steady - state behaviour of reaction networks , and introduces Pysces , a Python modelling toolkit .
OUTPUT:
| 12241066.txt |
software_name_mentions12 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Putting the 1991 census sample of anonymised records on your Unix workstation .
" The authors describe the development of a customised computer software package for easing the analysis of the U.K. 1991 Sample of Anonymised Records .
The resulting USAR [ Unix Sample of Anonymised Records ] package is designed to be portable within the Unix environment .
It offers a number of features such as interactive table design , intelligent data interpretation , and fuzzy query .
An example of SAR analysis is provided .
"
OUTPUT:
| 12346250.txt |
software_name_mentions13 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: FROST : a filter - based fold recognition method .
To assess the reliability of fold assignments to protein sequences , we developed a fold recognition method called FROST ( Fold Recognition - Oriented Search Tool ) based on a series of filters and a database specifically designed as a benchmark for this new method under realistic conditions .
This benchmark database consists of proteins for which there exists , at least , another protein with an extensively similar 3D structure in a database of representative 3D structures ( i.e. , more than 65 % of the residues in both proteins can be structurally aligned ) . Because the testing of our method must be carried out under conditions similar to those of real fold recognition experiments , no protein pair with sequence similarity detectable using standard sequence comparison methods such as FASTA is included in the benchmark database .
While using FROST , we achieved a coverage of 60 % for a rate of error of 1 % . To obtain a baseline for our method , we used PSI - BLAST and 3D - PSSM .
Under the same conditions , for a 1 % error rate , coverages for PSI - BLAST and 3D - PSSM were 33 and 56 % , respectively .
OUTPUT:
| 12402359.txt |
software_name_mentions14 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: The Xplor - NIH NMR molecular structure determination package .
We announce the availability of the Xplor - NIH software package for NMR biomolecular structure determination .
This package consists of the pre - existing XPLOR program , along with many NMR - specific extensions developed at the NIH .
In addition to many features which have been developed over the last 20 years , the Xplor - NIH package contains an interface with a new programmatic framework written in C + + . This interface currently supports the general purpose scripting languages Python and TCL , enabling rapid development of new tools , such as new potential energy terms and new optimization methods .
Support for these scripting languages also facilitates interaction with existing external programs for structure analysis , structure manipulation , visualization , and spectral analysis .
OUTPUT:
| 12565051.txt |
software_name_mentions15 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Creating a portable data - collection system with Microsoft Embedded Visual Tools for the Pocket PC .
This paper describes an overview and illustrative example for creating a portable data - collection system using Microsoft Embedded Visual Tools for the Pocket PC .
A description of the Visual Basic programming language is given , along with examples of computer code procedures for developing data - collection software .
Program specifications , strategies for customizing the collection system , and troubleshooting tips are also provided .
OUTPUT:
| 12858994.txt |
software_name_mentions16 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: CMAP : contig mapping and analysis package , a relational database for chromosome reconstruction .
In the contig mapping and analysis package , CMAP , we provide a foundation for reverse genetics by organizing information about DNA fragments obtained from an organism ' s genome into a physical map .
The user can store information about a particular segment of DNA .
This information can be both descriptive , such as any genes contained in a particular DNA fragment , or experimental , such as hybridization profiles or restriction digest patterns for comparison with other fragments .
The package can then be instructed to update the physical map or provide information on a DNA fragment within the map , such as its location .
The user interface is designed to minimize the learning curve associated with database usage , while eliminating the possibility of entering data outside the ranges of fields through error - checking protocols .
Queries are currently accomplished by the use of dynamic SQL ( structured query language ) , which gives the user the ability to build queries based on any combination of the attributes contained within the database without requiring that all possible queries be permanently programmed within the query software .
In order to eliminate the need for knowledge of SQL , an interface was designed to allow users to build queries by menu choices .
Thus , CMAP is a software package supporting a database for both the production and storage of a physical map as well as being the first step toward the production of a physical mapping workstation .
OUTPUT:
| 1422880.txt |
software_name_mentions17 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Prediction of protein secondary structure with a reliability score estimated by local sequence clustering .
Most algorithms for protein secondary structure prediction are based on machine learning techniques , e.g. neural networks .
Good architectures and learning methods have improved the performance continuously .
The introduction of profile methods , e.g .
PSI - BLAST , has been a major breakthrough in increasing the prediction accuracy to close to 80 % . In this paper , a brute - force algorithm is proposed and the reliability of each prediction is estimated by a z - score based on local sequence clustering .
This algorithm is intended to perform well for those secondary structures in a protein whose formation is mainly dominated by the neighboring sequences and short - range interactions .
A reliability z - score has been defined to estimate the goodness of a putative cluster found for a query sequence in a database .
The database for prediction was constructed by experimentally determined , non - redundant protein structures with < 25 % sequence homology , a list maintained by PDBSELECT .
Our test results have shown that this new algorithm , belonging to what is known as nearest neighbor methods , performed very well within the expectation of previous methods and that the reliability z - score as defined was correlated with the reliability of prediction .
This led to the possibility of making very accurate predictions for a few selected residues in a protein with an accuracy measure of Q3 > 80 % . The further development of this algorithm , and a nucleation mechanism for protein folding are suggested .
OUTPUT:
| 14560050.txt |
software_name_mentions18 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: GeneViTo : visualizing gene - product functional and structural features in genomic datasets .
BACKGROUND : The availability of increasing amounts of sequence data from completely sequenced genomes boosts the development of new computational methods for automated genome annotation and comparative genomics .
Therefore , there is a need for tools that facilitate the visualization of raw data and results produced by bioinformatics analysis , providing new means for interactive genome exploration .
Visual inspection can be used as a basis to assess the quality of various analysis algorithms and to aid in - depth genomic studies .
RESULTS : GeneViTo is a JAVA - based computer application that serves as a workbench for genome - wide analysis through visual interaction .
The application deals with various experimental information concerning both DNA and protein sequences ( derived from public sequence databases or proprietary data sources ) and meta - data obtained by various prediction algorithms , classification schemes or user - defined features .
Interaction with a Graphical User Interface ( GUI ) allows easy extraction of genomic and proteomic data referring to the sequence itself , sequence features , or general structural and functional features .
Emphasis is laid on the potential comparison between annotation and prediction data in order to offer a supplement to the provided information , especially in cases of " poor " annotation , or an evaluation of available predictions .
Moreover , desired information can be output in high quality JPEG image files for further elaboration and scientific use .
A compilation of properly formatted GeneViTo input data for demonstration is available to interested readers for two completely sequenced prokaryotes , Chlamydia trachomatis and Methanococcus jannaschii .
CONCLUSIONS : GeneViTo offers an inspectional view of genomic functional elements , concerning data stemming both from database annotation and analysis tools for an overall analysis of existing genomes .
The application is compatible with Linux or Windows ME - 2000 - XP operating systems , provided that the appropriate Java Runtime Environment is already installed in the system .
OUTPUT:
| 14594459.txt |
software_name_mentions19 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: MANIFOLD : protein fold recognition based on secondary structure , sequence similarity and enzyme classification .
We present a protein fold recognition method , MANIFOLD , which uses the similarity between target and template proteins in predicted secondary structure , sequence and enzyme code to predict the fold of the target protein .
We developed a non - linear ranking scheme in order to combine the scores of the three different similarity measures used .
For a difficult test set of proteins with very little sequence similarity , the program predicts the fold class correctly in 34 % of cases .
This is an over twofold increase in accuracy compared with sequence - based methods such as PSI - BLAST or GenTHREADER , which score 13 - 14 % correct first hits for the same test set .
The functional similarity term increases the prediction accuracy by up to 3 % compared with using the combination of secondary structure similarity and PSI - BLAST alone .
We argue that using functional and secondary structure information can increase the fold recognition beyond sequence similarity .
OUTPUT:
| 14631066.txt |
software_name_mentions20 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: GRIL : genome rearrangement and inversion locator .
UNLABELLED : GRIL is a tool to automatically identify collinear regions in a set of bacterial - size genome sequences .
GRIL uses three basic steps .
First , regions of high sequence identity are located .
Second , some of these regions are filtered based on user - specified criteria .
Finally , the remaining regions of sequence identity are used to define significant collinear regions among the sequences .
By locating collinear regions of sequence , GRIL provides a basis for multiple genome alignment using current alignment systems .
GRIL also provides a basis for using current inversion distance tools to infer phylogeny .
AVAILABILITY : GRIL is implemented in C + + and runs on any x86 - based Linux or Windows platform .
It is available from http : / / asap.ahabs.wisc.edu / gril
OUTPUT:
| 14693819.txt |
software_name_mentions21 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: APE : Analyses of Phylogenetics and Evolution in R language .
UNLABELLED : Analysis of Phylogenetics and Evolution ( APE ) is a package written in the R language for use in molecular evolution and phylogenetics .
APE provides both utility functions for reading and writing data and manipulating phylogenetic trees , as well as several advanced methods for phylogenetic and evolutionary analysis ( e.g. comparative and population genetic methods ) . APE takes advantage of the many R functions for statistics and graphics , and also provides a flexible framework for developing and implementing further statistical methods for the analysis of evolutionary processes .
AVAILABILITY : The program is free and available from the official R package archive at http : / / cran.r - project.org / src / contrib / PACKAGES.html # ape .
APE is licensed under the GNU General Public License .
OUTPUT:
| 14734327.txt |
software_name_mentions22 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Open source clustering software .
SUMMARY : We have implemented k - means clustering , hierarchical clustering and self - organizing maps in a single multipurpose open - source library of C routines , callable from other C and C + + programs .
Using this library , we have created an improved version of Michael Eisen ' s well - known Cluster program for Windows , Mac OS X and Linux / Unix .
In addition , we generated a Python and a Perl interface to the C Clustering Library , thereby combining the flexibility of a scripting language with the speed of C .
AVAILABILITY : The C Clustering Library and the corresponding Python C extension module Pycluster were released under the Python License , while the Perl module Algorithm : : Cluster was released under the Artistic License .
The GUI code Cluster 3.0 for Windows , Macintosh and Linux / Unix , as well as the corresponding command - line program , were released under the same license as the original Cluster code .
The complete source code is available at http : / / bonsai.ims.u - tokyo.ac.jp / mdehoon / software / cluster .
Alternatively , Algorithm : : Cluster can be downloaded from CPAN , while Pycluster is also available as part of the Biopython distribution .
OUTPUT:
| 14871861.txt |
software_name_mentions23 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: A computer program for measuring body size distortion and body dissatisfaction .
A computer program is described that measures body size distortion and body dissatisfaction .
The program is written using Visual Basic development tools and will run on any Windows 98 or more current system .
The width of a static digital image of the participant can be manipulated using three separate psychophysical methods .
In the method of adjustment , the participant adjusts the image wider or thinner to match his or her perceived size .
The participants may also be required to adjust the image to their ideal size , with the discrepancy between perceived and ideal size being used as a measure of body dissatisfaction .
In the staircase method , participants see an image that is continuously expanding or contracting .
The participants change the direction of the distortion when the image matches their perceived size .
In the adaptive probit estimation procedure , participants judge whether a static image is distorted too wide or too thin .
Analysis of the responses permits a determination of the point of subjective equality ( PSE ) and the difference limen ( DL ) values .
The DL reflects the amount of body size distortion necessary for the participant to detect the distortion 50 % of the time .
The PSE reflects the participant ' s subjective judgment of his or her body size .
These two values are reflective of the sensory and nonsensory components , respectively , that contribute to body size judgments .
OUTPUT:
| 15190703.txt |
software_name_mentions24 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: A computerized simulation for investigating gambling behavior during roulette play .
The present paper describes a computerized roulette program for the conducting of psychological research on gambling behavior .
The program was designed to simulate an actual roulette game found in casinos and gambling riverboats throughout North America .
The roulette program collects detailed trial - by - trial data on player / participant behavior that can easily be transferred into data analysis and graphics programs .
This multimedia simulation was designed in the Visual Basic programming language , and it is capable of running on any IBM - compatible personal computer running the Windows 2000 or higher operating system .
OUTPUT:
| 15190704.txt |
software_name_mentions25 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: [ Creation and clinical application of real - time dose monitor using dose area product meter ] . The management of patient dose has become more of an issue in recent years .
Dose can be determined non - invasively and in real time through the use of a dose area product meter , but it is the area dose value that is obtained .
Therefore , we created a program that estimates entrance skin dose ( ESD ) in real time from area dose values obtained during procedures .
We used Microsoft Visual C + + 6.0 ( Standard Edition ) for the programming language and C language for the programming environment .
The value was a maximum 285.4 mGy at ileus tube insertion when measuring ESD for radiography of the digestive organ and non - vascular type IVR using the created program and seeking the average according to the procedures .
The program that we created can be considered valid for monitoring ESD correctly and in real time .
OUTPUT:
| 15213700.txt |
software_name_mentions26 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: PROSPECT - PSPP : an automatic computational pipeline for protein structure prediction .
Knowledge of the detailed structure of a protein is crucial to our understanding of the biological functions of that protein .
The gap between the number of solved protein structures and the number of protein sequences continues to widen rapidly in the post - genomics era due to long and expensive processes for solving structures experimentally .
Computational prediction of structures from amino acid sequence has come to play a key role in narrowing the gap and has been successful in providing useful information for the biological research community .
We have developed a prediction pipeline , PROSPECT - PSPP , an integration of multiple computational tools , for fully automated protein structure prediction .
The pipeline consists of tools for ( i ) preprocessing of protein sequences , which includes signal peptide prediction , protein type prediction ( membrane or soluble ) and protein domain partition , ( ii ) secondary structure prediction , ( iii ) fold recognition and ( iv ) atomic structural model generation .
The centerpiece of the pipeline is our threading - based program PROSPECT .
The pipeline is implemented using SOAP ( Simple Object Access Protocol ) , which makes it easier to share our tools and resources .
The pipeline has an easy - to - use user interface and is implemented on a 64 - node dual processor Linux cluster .
It can be used for genome - scale protein structure prediction .
The pipeline is accessible at http : / / csbl.bmb.uga.edu / protein _ pipeline .
OUTPUT:
| 15215441.txt |
software_name_mentions27 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Software for advanced HRV analysis .
A computer program for advanced heart rate variability ( HRV ) analysis is presented .
The program calculates all the commonly used time - and frequency - domain measures of HRV as well as the nonlinear Poincar plot .
In frequency - domain analysis parametric and nonparametric spectrum estimates are calculated .
The program generates an informative printable report sheet which can be exported to various file formats including the portable document format ( PDF ) . Results can also be saved as an ASCII file from which they can be imported to a spreadsheet program such as the Microsoft Excel .
Together with a modern heart rate monitor capable of recording RR intervals this freely distributed program forms a complete low - cost HRV measuring and analysis system .
OUTPUT:
| 15313543.txt |
software_name_mentions28 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: HICOPS : human interface computer program in space .
BACKGROUND : During long experimental set ups , a protocol book usually guides cosmonauts .
This is not very easy to work with in microgravity conditions and is not very efficient .
For the cardiovascular physiology experiment CARDIOCOG during the Belgian Soyuz Mission ( Odissea , November 2002 ) we developed a software program that guided the cosmonauts through the experiment .
The software was developed in LabVIEW , thoroughly tested by CNES and the Russian space authorities and transported to the ISS as a stand - alone application .
An adapted version was used during the Spanish Cervantes Mission in October 2003. RESULTS : This program provided several advantages : ( 1 ) error procedures could be easily dealt with in using the program ' s incorporated error structure ; ( 2 ) the experimental sequences were easy to follow for the cosmonauts ; ( 3 ) the experimental duration was exactly the same for all repetitions of the experiment , since the program imposed the timing ; ( 4 ) after the flight , we were able to reconstruct all sequences of the experiment using a log - file that was automatically created during the different steps of the experiment ; and ( 5 ) we were able to impose exact breathing frequencies to the cosmonauts using a visual aid .
CONCLUSION : Less training was necessary for the cosmonauts to learn the experiment .
Reconstruction of the experiment timing was easy .
Exact breathing frequencies were obtained at each repetition .
The program HICOPS worked to the overall satisfaction of the cosmonauts and they preferred working with HICOPS instead of with paper flow sheets .
Data for the cardiovascular experiment during both missions were obtained in a standardised way .
OUTPUT:
| 15362274.txt |
software_name_mentions29 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Software suite for image archiving and retrieval .
The efficient operation of a clinical picture archiving and communication system ( PACS ) requires a fully functional image archive .
The archive should not only be able to store and retrieve images reliably but should also work in concert with software that optimizes the flow of image - related information throughout the PACS .
The authors devised a software suite that serves to improve the flow and content of information and the integrity of the data .
The software was developed by translating the functions performed by a conventional film library .
Types of transactions possible with this system include scheduling , canceling , rescheduling , and prestaging examinations ; changing demographic information ; merging patient information ; and generating reports .
The authors believe such a software suite is essential for a clinically useful PACS .
OUTPUT:
| 1561425.txt |
software_name_mentions30 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: [ The new features of the bibliography database manager EndNote 6.0 and 7.0 ] . The bibliography database manager EndNote shows since 1999 a dramatically technical development .
A new improved version is published every year .
Because the version 7.0 was recently ( June 2003 ) released , we want to describe some aspects of the new version .
We also would like to examine , for which user the use of the respective version is recommendable .
The use of the software package EndNote 6.0 and also 7.0 for Windows is described .
The main reason for getting Endnote 6 is its clearly improved functions and features : Organize a variety of charts equations or pictures and the use of Microsoft - templates .
The version 7.0 can be recommended especially to scientist who much works with a Palm particularly .
It is also possible to work not only with Microsoft - Word but also with other word processors and creating a bibliography with topic headings .
Altogether EndNote 6.0 and also 7.0 provides an excellent combination of features and ease of use .
The versions 6.0 or 7.0 are useful especially for people , who every single day use EndNote .
EndNote 7.0 is for user of a Palm a special recommendable version .
OUTPUT:
| 15794361.txt |
software_name_mentions31 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: AutoLink : automated sequential resonance assignment of biopolymers from NMR data by relative - hypothesis - prioritization - based simulated logic .
We have developed a new computer algorithm for determining the backbone resonance assignments for biopolymers .
The approach we have taken , relative hypothesis prioritization , is implemented as a Lua program interfaced to the recently developed computer - aided resonance assignment ( CARA ) program .
Our program can work with virtually any spectrum type , and is especially good with NOESY data .
The results of the program are displayed in an easy - to - read , color - coded , graphic representation , allowing users to assess the quality of the results in minutes .
Here we report the application of the program to two RNA recognition motifs of Apobec - 1 Complementation Factor .
The assignment of these domains demonstrates AutoLink ' s ability to deliver accurate resonance assignments from very minimal data and with minimal user intervention .
OUTPUT:
| 15809181.txt |
software_name_mentions32 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Stochastic reaction - diffusion simulation with MesoRD .
UNLABELLED : MesoRD is a tool for stochastic simulation of chemical reactions and diffusion .
In particular , it is an implementation of the next subvolume method , which is an exact method to simulate the Markov process corresponding to the reaction - diffusion master equation .
AVAILABILITY : MesoRD is free software , written in C + + and licensed under the GNU general public license ( GPL ) . MesoRD runs on Linux , Mac OS X , NetBSD , Solaris and Windows XP .
It can be downloaded from http : / / mesord.sourceforge.net .
CONTACT : johan.elf @ icm.uu.se ; johan.hattne @ embl - hamburg.de SUPPLEMENTARY INFORMATION : ' MesoRD User ' s Guide ' and other documents are available at http : / / mesord.sourceforge.net.
OUTPUT:
| 15817692.txt |
software_name_mentions33 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: COTRANS : a program for cotransduction analysis .
COTRANS is a program for analyzing cotransduction data .
It calculates distances from pairwise cotransduction frequencies , computes crossovers required to obtain each observed recombinant class , and applies rules to draw conclusions about order .
The rules are based on the correlation between the frequency of the classes and the number of required crossovers for each possible ordering compatible with the distance calculations .
The procedure emulates a geneticist ' s stepwise analysis of the data by first calculating distances , then looking for obvious three - point ordering conclusions , and finally proceeding to a complete crossover analysis .
It reports results from each step of the analysis and an overall conclusion .
COTRANS provides significant gains in speed and convenience over hand analysis , particularly for multipoint crosses with several recombinant classes .
OUTPUT:
| 1592240.txt |
software_name_mentions34 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Approaching the taxonomic affiliation of unidentified sequences in public databases - - an example from the mycorrhizal fungi .
BACKGROUND : During the last few years , DNA sequence analysis has become one of the primary means of taxonomic identification of species , particularly so for species that are minute or otherwise lack distinct , readily obtainable morphological characters .
Although the number of sequences available for comparison in public databases such as GenBank increases exponentially , only a minuscule fraction of all organisms have been sequenced , leaving taxon sampling a momentous problem for sequence - based taxonomic identification .
When querying GenBank with a set of unidentified sequences , a considerable proportion typically lack fully identified matches , forming an ever - mounting pile of sequences that the researcher will have to monitor manually in the hope that new , clarifying sequences have been submitted by other researchers .
To alleviate these concerns , a project to automatically monitor select unidentified sequences in GenBank for taxonomic progress through repeated local BLAST searches was initiated .
Mycorrhizal fungi - - a field where species identification often is prohibitively complex - - and the much used ITS locus were chosen as test bed .
RESULTS : A Perl script package called emerencia is presented .
On a regular basis , it downloads select sequences from GenBank , separates the identified sequences from those insufficiently identified , and performs BLAST searches between these two datasets , storing all results in an SQL database .
On the accompanying web - service http : / / emerencia.math.chalmers.se , users can monitor the taxonomic progress of insufficiently identified sequences over time , either through active searches or by signing up for e - mail notification upon disclosure of better matches .
Other search categories , such as listing all insufficiently identified sequences ( and their present best fully identified matches ) publication - wise , are also available .
DISCUSSION : The ever - increasing use of DNA sequences for identification purposes largely falls back on the assumption that public sequence databases contain a thorough sampling of taxonomically well - annotated sequences .
Taxonomy , held by some to be an old - fashioned trade , has accordingly never been more important .
emerencia does not automate the taxonomic process , but it does allow researchers to focus their efforts elsewhere than countless manual BLAST runs and arduous sieving of BLAST hit lists .
The emerencia system is available on an open source basis for local installation with any organism and gene group as targets .
OUTPUT:
| 16022740.txt |
software_name_mentions35 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Estimating aquifer transmissivity from specific capacity using MATLAB .
Historically , specific capacity information has been used to calculate aquifer transmissivity when pumping test data are unavailable .
This paper presents a simple computer program written in the MATLAB programming language that estimates transmissivity from specific capacity data while correcting for aquifer partial penetration and well efficiency .
The program graphically plots transmissivity as a function of these factors so that the user can visually estimate their relative importance in a particular application .
The program is compatible with any computer operating system running MATLAB , including Windows , Macintosh OS , Linux , and Unix .
Two simple examples illustrate program usage .
OUTPUT:
| 16029186.txt |
software_name_mentions36 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Squid BACKGROUND : BLAST is a widely used genetic research tool for analysis of similarity between nucleotide and protein sequences .
This paper presents a software application entitled " Squid " that makes use of grid technology .
The current version , as an example , is configured for BLAST applications , but adaptation for other computing intensive repetitive tasks can be easily accomplished in the open source version .
This enables the allocation of remote resources to perform distributed computing , making large BLAST queries viable without the need of high - end computers .
RESULTS : Most distributed computing / grid solutions have complex installation procedures requiring a computer specialist , or have limitations regarding operating systems .
Squid is a multi - platform , open - source program designed to " keep things simple " while offering high - end computing power for large scale applications .
Squid also has an efficient fault tolerance and crash recovery system against data loss , being able to re - route jobs upon node failure and recover even if the master machine fails .
Our results show that a Squid application , working with N nodes and proper network resources , can process BLAST queries almost N times faster than if working with only one computer .
CONCLUSION : Squid offers high - end computing , even for the non - specialist , and is freely available at the project web site .
Its open - source and binary Windows distributions contain detailed instructions and a " plug - n - play " instalation containing a pre - configured example .
OUTPUT:
| 16078998.txt |
software_name_mentions37 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: ROCPLOT : a generic software tool for ROC analysis and the validation of predictive methods .
UNLABELLED : Receiver operating characteristic ( ROC ) analysis is a powerful and widely used technique for assessing predictive methods , yet there are no generic , open - source software tools for this that are freely available .
Our ROCPLOT program performs ROC analysis on one or more files of search results ( hits ) and generates the following : ( i ) ROC values , giving a convenient numerical measure of method sensitivity and specificity ; ( ii ) ROC plots graphically displaying sensitivity and specificity ; ( iii ) classification plots to aid interpretation of the ROC plots and values ; and ( iv ) a bar chart of the distribution of ROC values .
ROCPLOT is generic and flexible : data in multiple hits files can be processed in series or parallel , allowing the results of multiple predictions to be viewed side - by - side or combined .
AVAILABILITY : ROCPLOT is freely available for download as part of the European Molecular Biology Open Software Suite , EMBOSS ( http : / / emboss.sourceforge.net / apps / rocplot.html ) .
OUTPUT:
| 16128614.txt |
software_name_mentions38 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: POEM : Parameter Optimization using Ensemble Methods : application to target specific scoring functions .
In computational biology processes such as docking , binding , and folding are often described by simplified , empirical models .
These models are fitted to physical properties of the process by adjustable parameters .
An appropriate choice of these parameters is crucial for the quality of the models .
Locating the best choices for the parameters is often is a difficult task , depending on the complexity of the model .
We describe a new method and program , POEM ( Parameter Optimization using Ensemble Methods ) , for this task .
In POEM we combine the DOE ( Design Of Experiment ) procedure with ensembles of different regression methods .
We apply the method to the optimization of target specific scoring functions in molecular docking .
The method consists of an iterative procedure that uses alternate evaluation and prediction steps .
During each cycle of optimization we fit an approximate function to a defined loss function landscape and improve the quality of this fit from cycle to cycle by constantly augmenting our data set .
As test applications we fitted the FlexX and Screenscore scoring functions to the kinase and ATPase protein classes .
The results are promising : Starting from random parameters we are able to locate parameter sets which show superior performance compared to the original values .
The POEM approach converges quickly and the approximated loss function landscapes are smooth , thus making the approach a suitable method for optimizations on rugged landscapes .
OUTPUT:
| 16180906.txt |
software_name_mentions39 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: GenoMiner : a tool for genome - wide search of coding and non - coding conserved sequence tags .
GenoMiner is a software tool that searches for regions of similarity between user - submitted genome or transcript sequences and user - specified whole genome assemblies .
The program then identifies conserved sequence tags ( CSTs ) in these homologous regions and provides a prediction of their coding or non - coding nature .
The analysis is carried out through three steps : ( 1 ) definition of sequence regions homologous to the query sequence in the selected target genomes by a fast BLAT alignment ; ( 2 ) identification of CSTs by a more sensitive BLAST - like alignment between the query and the homologous regions in the target genomes and ( 3 ) assessment of the coding or non - coding nature of detected CSTs through the computation of a suitable coding potential score .
GenoMiner allows the user to search the query sequence against a number of vertebrate genome assemblies in a single run providing a user - friendly graphical output .
OUTPUT:
| 16267081.txt |
software_name_mentions40 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: ChIPOTle : a user - friendly tool for the analysis of ChIP - chip data .
ChIPOTle ( Chromatin ImmunoPrecipitation On Tiled arrays ) takes advantage of two unique properties of ChIP - chip data : the single - tailed nature of the data , caused by specific enrichment but not specific depletion of genomic fragments ; and the predictable enrichment of DNA fragments adjacent to sites of direct protein - DNA interaction .
Implemented as a Microsoft Excel macro written in Visual Basic , ChIPOTle uses a sliding window approach that yields improvements in the identification of bona fide sites of protein - DNA interaction .
OUTPUT:
| 16277752.txt |
software_name_mentions41 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: PCP : a program for supervised classification of gene expression profiles .
UNLABELLED : PCP ( Pattern Classification Program ) is an open - source machine learning program for supervised classification of patterns ( vectors of measurements ) . The principal use of PCP in bioinformatics is design and evaluation of classifiers for use in clinical diagnostic tests based on measurements of gene expression .
PCP implements leading pattern classification and gene selection algorithms and incorporates cross - validation estimation of classifier performance .
Importantly , the implementation integrates gene selection and class prediction stages , which is vital for computing reliable performance estimates in small - sample scenarios .
Additionally , the program includes automated and efficient model selection ( optimization of parameters ) for support vector machine ( SVM ) classifier .
The distribution includes Linux and Windows / Cygwin binaries .
The program can easily be ported to other platforms .
AVAILABILITY : Free download at http : / / pcp.sourceforge.net
OUTPUT:
| 16278240.txt |
software_name_mentions42 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: [ Product of the month : a bibliographic database with optional formatting capability ] . The function and usage of the software package " Endnote Plus " for the Apple Macintosh are described .
Its advantages in fulfilling different requirements for the citation style and the sort order of reference lists are emphasized .
OUTPUT:
| 1635986.txt |
software_name_mentions43 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Improvement in the accuracy of multiple sequence alignment program MAFFT .
In 2002 , we developed and released a rapid multiple sequence alignment program MAFFT that was designed to handle a huge ( up to approximately 5 , 000 sequences ) and long data ( approximately 2 , 000 aa or approximately 5 , 000 nt ) in a reasonable time on a standard desktop PC .
As for the accuracy , however , the previous versions ( v.4 and lower ) of MAFFT were outperformed by ProbCons and TCoffee v.2 , both of which were released in 2004 , in several benchmark tests .
Here we report a recent extension of MAFFT that aims to improve the accuracy with as little cost of calculation time as possible .
The extended version of MAFFT ( v.5 ) has new iterative refinement options , G - INS - i and L - INS - i ( collectively denoted as [ GL ] - INS - i in this report ) . These options use a new objective function combining the weighted sum - of - pairs ( WSP ) score and a score similar to COFFEE derived from all pairwise alignments .
We discuss the improvement in accuracy brought by this extension , mainly using two benchmark tests released very recently , BAliBASE v.3 ( for protein alignments ) and BRAliBASE ( for RNA alignments ) . According to BAliBASE v.3 , the overall average accuracy of L - INS - i was higher than those of other methods successively released in 2004 , although the difference among the most accurate methods ( ProbCons , TCoffee v.2 and new options of MAFFT ) was small .
The advantage in accuracy of [ GL ] - INS - i became greater for the alignments consisting of approximately 50 - 100 sequences .
By utilizing this feature of MAFFT , we also examined another possible approach to improve the accuracy by incorporating homolog information collected from database .
The [ GL ] - INS - i options are applicable to aligning up to approximately 200 sequences , although not applicable to thousands of sequences because of time and space complexities .
OUTPUT:
| 16362903.txt |
software_name_mentions44 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: A high level interface to SCOP and ASTRAL implemented in python .
BACKGROUND : Benchmarking algorithms in structural bioinformatics often involves the construction of datasets of proteins with given sequence and structural properties .
The SCOP database is a manually curated structural classification which groups together proteins on the basis of structural similarity .
The ASTRAL compendium provides non redundant subsets of SCOP domains on the basis of sequence similarity such that no two domains in a given subset share more than a defined degree of sequence similarity .
Taken together these two resources provide a ' ground truth ' for assessing structural bioinformatics algorithms .
We present a small and easy to use API written in python to enable construction of datasets from these resources .
RESULTS : We have designed a set of python modules to provide an abstraction of the SCOP and ASTRAL databases .
The modules are designed to work as part of the Biopython distribution .
Python users can now manipulate and use the SCOP hierarchy from within python programs , and use ASTRAL to return sequences of domains in SCOP , as well as clustered representations of SCOP from ASTRAL .
CONCLUSION : The modules make the analysis and generation of datasets for use in structural genomics easier and more principled .
OUTPUT:
| 16403221.txt |
software_name_mentions45 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: WinPop 2.5 : software for representing population genetics phenomena .
The curriculum for genetics courses is shifting from a classical to a more molecular genetics focus , increasing the importance of subjects such as population genetics .
Population genetics is a computational and statistical field that requires a good understanding of the nature of stochastic events .
It is a difficult field for biology students with a limited mathematical background and there is a need for visualisation tools to facilitate understanding by the use of practical examples .
WinPop provides students and researchers with a visual tool to allow the simulation and representation of population genetics phenomena .
WinPop is a user - friendly software meant for use in population genetics courses and basic research .
WinPop 2.5 contains six different modules that represent and simulate population genetics models .
Genotype and allele frequencies are calculated under the different models : panmixia , genetic drift , assortative matings , selection , gene flow and mutation .
The program ' s interface presents information in Cartesian graphics and isosceles triangular coordinate systems , allowing the user to save graphical and textual data output from the simulations .
WinPop is developed in Visual Basic 6.0 and uses Windows 95 and higher .
WinPop 2.5 can be downloaded from http : / / www.genedrift.org / winpop.php.
OUTPUT:
| 16420737.txt |
software_name_mentions46 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Comparison of human ( and other ) genome browsers .
The sequence of the human genome provides a scaffold on which numerous annotations , such the locations of genes , can be laid .
Genome browsers have been created to allow the simultaneous display of multiple annotations within a graphical interface .
In addition , they provide the ability to search for markers and sequences , to extract annotations for specific regions or for the whole genome and to act as a central starting point for genomic research .
This review describes the basic functionality of genome browsers and compares three of them : the University of California Santa Cruz ( UCSC ) Genome Browser , the Ensembl Genome Browser and the NCBI MapViewer .
OUTPUT:
| 16460652.txt |
software_name_mentions47 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Confirmatory factor analysis using Microsoft Excel .
This article presents a method for using Microsoft ( MS ) Excel for confirmatory factor analysis ( CFA ) . CFA is often seen as an impenetrable technique , and thus , when it is taught , there is frequently little explanation of the mechanisms or underlying calculations .
The aim of this article is to demonstrate that this is not the case ; it is relatively straightforward to produce a spreadsheet in MS Excel that can carry out simple CFA .
It is possible , with few or no programming skills , to effectively program a CFA analysis and , thus , to gain insight into the workings of the procedure .
OUTPUT:
| 16629301.txt |
software_name_mentions48 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: TopoICE - R : 3D visualization modeling the topology of DNA recombination .
UNLABELLED : TopoICE - R is a three - dimensional visualization and manipulation software for solving 2 - string tangle equations and can be used to model the topology of DNA bound by proteins such as recombinases and topoisomerases .
AVAILABILITY : This software , manual and example files are available at www.knotplot.com / download for Linux , Windows and Mac .
OUTPUT:
| 16672259.txt |
software_name_mentions49 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: VICMpred : an SVM - based method for the prediction of functional proteins of Gram - negative bacteria using amino acid patterns and composition .
In this study , an attempt has been made to predict the major functions of gram - negative bacterial proteins from their amino acid sequences .
The dataset used for training and testing consists of 670 non - redundant gram - negative bacterial proteins ( 255 of cellular process , 60 of information molecules , 285 of metabolism , and 70 of virulence factors ) . First we developed an SVM - based method using amino acid and dipeptide composition and achieved the overall accuracy of 52.39 % and 47.01 % , respectively .
We introduced a new concept for the classification of proteins based on tetrapeptides , in which we identified the unique tetrapeptides significantly found in a class of proteins .
These tetrapeptides were used as the input feature for predicting the function of a protein and achieved the overall accuracy of 68.66 % . We also developed a hybrid method in which the tetrapeptide information was used with amino acid composition and achieved the overall accuracy of 70.75 % . A five - fold cross validation was used to evaluate the performance of these methods .
The web server VICMpred has been developed for predicting the function of gram - negative bacterial proteins ( http : / / www.imtech.res.in / raghava / vicmpred / ) .
OUTPUT:
| 16689701.txt |
software_name_mentions50 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Structure determination of the Antp ( C39 - - - - S ) homeodomain from nuclear magnetic resonance data in solution using a novel strategy for the structure calculation with the programs DIANA , CALIBA , HABAS and GLOMSA .
The structure of a mutant Antennapedia homeodomain , Antp ( C39 - - - - S ) , from Drosophila melanogaster was determined using a set of new programs introduced in the accompanying paper .
An input dataset of 957 distance constraints and 171 dihedral angle constraints was collected using two - dimensional n.m.r. experiments with the 15N - labeled protein .
The resulting high quality structure for Antp ( C39 - - - - S ) , with an average root - mean - square deviation of 0.53 A between the backbone atoms of residues 7 to 59 in 20 energy - refined distance geometry structures and the mean structure , is nearly identical to the previously reported structure of the wild - type Antp homeodomain .
The only significant difference is in the connection between helices III and IV , which was found to be less kinked than was indicated by the structure determination for Antp .
The main emphasis of the presentation in this paper is on a detailed account of the practical use of a novel strategy for the computation of nuclear magnetic resonance structures of proteins with the combined use of the programs DIANA , CALIBA , HABAS and GLOMSA .
OUTPUT:
| 1671604.txt |
software_name_mentions51 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: The MPI Bioinformatics Toolkit for protein sequence analysis.
The MPI Bioinformatics Toolkit is an interactive web service which offers access
to a great variety of public and in-house bioinformatics tools. They are grouped
into different sections that support sequence searches, multiple alignment,
secondary and tertiary structure prediction and classification. Several public
tools are offered in customized versions that extend their functionality. For
example, PSI-BLAST can be run against regularly updated standard databases,
customized user databases or selectable sets of genomes. Another tool, Quick2D,
integrates the results of various secondary structure, transmembrane and disorder
prediction programs into one view. The Toolkit provides a friendly and intuitive
user interface with an online help facility. As a key feature, various tools are
interconnected so that the results of one tool can be forwarded to other tools.
One could run PSI-BLAST, parse out a multiple alignment of selected hits and send
the results to a cluster analysis tool. The Toolkit framework and the tools
developed in-house will be packaged and freely available under the GNU Lesser
General Public Licence (LGPL). The Toolkit can be accessed at
http://toolkit.tuebingen.mpg.de.
OUTPUT:
| 16845021.txt |
software_name_mentions52 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Vertebrate gene finding from multiple - species alignments using a two - level strategy .
BACKGROUND : One way in which the accuracy of gene structure prediction in vertebrate DNA sequences can be improved is by analyzing alignments with multiple related species , since functional regions of genes tend to be more conserved .
RESULTS : We describe DOGFISH , a vertebrate gene finder consisting of a cleanly separated site classifier and structure predictor .
The classifier scores potential splice sites and other features , using sequence alignments between multiple vertebrate species , while the structure predictor hypothesizes coding transcripts by combining these scores using a simple model of gene structure .
This also identifies and assigns confidence scores to possible additional exons .
Performance is assessed on the ENCODE regions .
We predict transcripts and exons across the whole human genome , and identify over 10 , 000 high confidence new coding exons not in the Ensembl gene set .
CONCLUSION : We present a practical multiple species gene prediction method .
Accuracy improves as additional species , up to at least eight , are introduced .
The novel predictions of the whole - genome scan should support efficient experimental verification .
OUTPUT:
| 16925840.txt |
software_name_mentions53 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: MUMMALS : multiple sequence alignment improved by using hidden Markov models with local structural information .
We have developed MUMMALS , a program to construct multiple protein sequence alignment using probabilistic consistency .
MUMMALS improves alignment quality by using pairwise alignment hidden Markov models ( HMMs ) with multiple match states that describe local structural information without exploiting explicit structure predictions .
Parameters for such models have been estimated from a large library of structure - based alignments .
We show that ( i ) on remote homologs , MUMMALS achieves statistically best accuracy among several leading aligners , such as ProbCons , MAFFT and MUSCLE , albeit the average improvement is small , in the order of several percent ; ( ii ) a large collection ( > 10 000 ) of automatically computed pairwise structure alignments of divergent protein domains is superior to smaller but carefully curated datasets for estimation of alignment parameters and performance tests ; ( iii ) reference - independent evaluation of alignment quality using sequence alignment - dependent structure superpositions correlates well with reference - dependent evaluation that compares sequence - based alignments to structure - based reference alignments .
OUTPUT:
| 16936316.txt |
software_name_mentions54 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: The Trial Protocol Tool: The PRACTIHC software tool that supported the writing of
protocols for pragmatic randomized controlled trials.
OBJECTIVE: To develop a tool that would make it easier for researchers,
especially those in low- and middle-income countries, to write research protocols
for pragmatic randomized controlled trials.
STUDY DESIGN AND SETTING: A series of focus groups was held at the inaugural
meeting of the Pragmatic RAndomized Controlled Trials in Health Care (PRACTIHC)
project in 2001 to develop a desired specification for the Trial Protocol Tool. A
working group of five individuals from the PRACTIHC group was formed to develop
content for the tool.
RESULTS: The Trial Protocol Tool was developed in English and Spanish as a
Microsoft Windows HTML help system. A Web-based version is also available. This
main body of the tool provides information, advice, and resources about the major
headings that should be part of every research protocol. Illustrative examples
are used throughout and are taken directly from the tool's protocol library.
Additional resources include checklists, programs (e.g., a sample size
calculator), and example documents (e.g., patient information leaflets).
CONCLUSION: The Trial Protocol Tool packages all the key requirements for the
development of a research protocol into one resource. We believe that the use of
the tool will help researchers to design effective trials and to write
high-quality protocols.
OUTPUT:
| 17027422.txt |
software_name_mentions55 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Comparison of simulated periodontal bone defect depth measured in digital
radiographs in dedicated and non-dedicated software systems.
OBJECTIVES: To compare simulated periodontal bone defect depth measured in
digital radiographs with dedicated and non-dedicated software systems and to
compare the depth measurements from each program with the measurements in dry
mandibles.
METHODS: Forty periodontal bone defects were created at the proximal area of the
first premolar in dry pig mandibles. Measurements of the defects were performed
with a periodontal probe in the dry mandible. Periapical digital radiographs of
the defects were recorded using the Schick sensor in a standardized exposure
setting. All images were read using a Schick dedicated software system (CDR DICOM
for Windows v.3.5), and three commonly available non-dedicated software systems
(Vix Win 2000 v.1.2; Adobe Photoshop 7.0 and Image Tool 3.0). The defects were
measured three times in each image and a consensus was reached among three
examiners using the four software systems. The difference between the
radiographic measurements was analysed using analysis of variance (ANOVA) and by
comparing the measurements from each software system with the dry mandibles
measurements using Student's t-test.
RESULTS: The mean values of the bone defects measured in the radiographs were
5.07 mm, 5.06 mm, 5.01 mm and 5.11 mm for CDR Digital Image and Communication in
Medicine (DICOM) for Windows, Vix Win, Adobe Photoshop, and Image Tool,
respectively, and 6.67 mm for the dry mandible. The means of the measurements
performed in the four software systems were not significantly different, ANOVA (P
= 0.958). A significant underestimation of defect depth was obtained when we
compared the mean depths from each software system with the dry mandible
measurements (t-test; P approximately equal to 0.000).
CONCLUSIONS: The periodontal bone defect measurements in dedicated and in three
non-dedicated software systems were not significantly different, but they all
underestimated the measurements when compared with the measurements obtained in
the dry mandibles.
OUTPUT:
| 17082333.txt |
software_name_mentions56 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: A comparison of cellular irradiation techniques with alpha particles using the
Geant4 Monte Carlo simulation toolkit.
A comparison of three cellular irradiation techniques using the Monte Carlo
simulation toolkit Geant4 is presented in this paper. They involve
electrodeposited source of alpha particle-emitting radionuclides, random
classical alpha beam irradiation and single cell targeted irradiation using a
focused alpha microbeam line. The simulation allows the calculation of hit
distributions among the cellular population as well as the absorbed dose for two
typical cellular geometries.
OUTPUT:
| 17132663.txt |
software_name_mentions57 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Self-test software for PowerPoint: a tool for self-learning.
RATIONALE AND OBJECTIVES: We developed self-test software to improve
self-learning efficiency using Microsoft PowerPoint data files.
CONCLUSION: This tool can be run on IBM-compatible computer under Microsoft
Windows. It is a new useful and interactive tool for self-learning. This tool
allows users to do view the cases in the PowerPoint data files by random or
sequentially. Goal-oriented effective self-learning is possible from methods that
conjecture the possible differential diagnosis without promptly seeing correct
diagnosis. Thus effective and interactive self-learning is possible.
OUTPUT:
| 17138122.txt |
software_name_mentions58 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Room for interpretation .
Significant subtleties pose stumbling blocks for Eclipse ' s Open Healthcare Framework .
OUTPUT:
| 17144326.txt |
software_name_mentions59 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: cBrother : relaxing parental tree assumptions for Bayesian recombination detection .
Bayesian multiple change - point models accurately detect recombination in molecular sequence data .
Previous Java - based implementations assume a fixed topology for the representative parental data .
cBrother is a novel C language implementation that capitalizes on reduced computational time to relax the fixed tree assumption .
We show that cBrother is 19 times faster than its predecessor and the fixed tree assumption can influence estimates of recombination in a medically - relevant dataset .
Availability : cBrother can be freely downloaded from http : / / www.biomath.org / dormanks / and can be compiled on Linux , Macintosh and Windows operating systems .
Online documentation and a tutorial are also available at the site .
OUTPUT:
| 17145740.txt |
software_name_mentions60 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: GGtools : analysis of genetics of gene expression in bioconductor .
This paper reviews the central concepts and implementation of data structures and methods for studying genetics of gene expression with the GGtools package of Bioconductor .
Illustration with a HapMap + expression dataset is provided .
Availability : Package GGtools is part of Bioconductor 1.9 ( http : / / bioconductor.org ) . Open source with Artistic License .
OUTPUT:
| 17158513.txt |
software_name_mentions61 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Rapid SNP diagnostics using asymmetric isothermal amplification and a new mismatch - suppression technology .
We developed a rapid single nucleotide polymorphism ( SNP ) detection system named smart amplification process version 2 ( SMAP 2 ) . Because DNA amplification only occurred with a perfect primer match , amplification alone was sufficient to identify the target allele .
To achieve the requisite fidelity to support this claim , we used two new and complementary approaches to suppress exponential background DNA amplification that resulted from mispriming events .
SMAP 2 is isothermal and achieved SNP detection from whole human blood in 30 min when performed with a new DNA polymerase that was cloned and isolated from Alicyclobacillus acidocaldarius ( Aac pol ) . Furthermore , to assist the scientific community in configuring SMAP 2 assays , we developed software specific for SMAP 2 primer design .
With these new tools , a high - precision and rapid DNA amplification technology becomes available to aid in pharmacogenomic research and molecular - diagnostics applications .
OUTPUT:
| 17322893.txt |
software_name_mentions62 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Cone-beam micro-CT system based on LabVIEW software.
Construction of a cone-beam computed tomography (CBCT) system for laboratory
research usually requires integration of different software and hardware
components. As a result, building and operating such a complex system require the
expertise of researchers with significantly different backgrounds. Additionally,
writing flexible code to control the hardware components of a CBCT system
combined with designing a friendly graphical user interface (GUI) can be
cumbersome and time consuming. An intuitive and flexible program structure, as
well as the program GUI for CBCT acquisition, is presented in this note. The
program was developed in National Instrument's Laboratory Virtual Instrumentation
Engineering Workbench (LabVIEW) graphical language and is designed to control a
custom-built CBCT system but has been also used in a standard angiographic suite.
The hardware components are commercially available to researchers and are in
general provided with software drivers which are LabVIEW compatible. The program
structure was designed as a sequential chain. Each step in the chain takes care
of one or two hardware commands at a time; the execution of the sequence can be
modified according to the CBCT system design. We have scanned and reconstructed
over 200 specimens using this interface and present three examples which cover
different areas of interest encountered in laboratory research. The resulting 3D
data are rendered using a commercial workstation. The program described in this
paper is available for use or improvement by other researchers.
OUTPUT:
| 17333411.txt |
software_name_mentions63 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: SNPTools: a software tool for visualization and analysis of microarray data.
SUMMARY: We have created a software tool, SNPTools, for analysis and
visualization of microarray data, mainly SNP array data. The software can analyse
and find differences in intensity levels between groups of arrays and identify
segments of SNPs (genes, clones), where the intensity levels differ significantly
between the groups. In addition, SNPTools can show jointly loss-of-heterozygosity
(LOH) data (derived from genotypes) and intensity data for paired samples of
tumour and normal arrays. The output graphs can be manipulated in various ways to
modify and adjust the layout. A wizard allows options and parameters to be
changed easily and graphs replotted. All output can be saved in various formats,
and also re-opened in SNPTools for further analysis. For explorative use,
SNPTools allows various genome information to be loaded onto the graphs.
AVAILABILITY: The software, example data sets and tutorials are freely available
from http://www.birc.au.dk/snptools
OUTPUT:
| 17384422.txt |
software_name_mentions64 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Development of an educational 'toolkit' for health professionals and their
patients with prediabetes: the WAKEUP study (Ways of Addressing Knowledge
Education and Understanding in Pre-diabetes).
AIMS: To identify key messages about pre-diabetes and to design, develop and
pilot an educational toolkit to address the information needs of patients and
health professionals.
METHODS: Mixed qualitative methodology within an action research framework. Focus
group interviews with patients and health professionals and discussion with an
expert reference group aimed to identify the important messages and produce a
draft toolkit. Two action research cycles were then conducted in two general
practices, during which the draft toolkit was used and video-taped consultations
and follow-up patient interviews provided further data. Framework analysis
techniques were used to examine the data and to elicit action points for
improving the toolkit.
RESULTS: The key messages about pre-diabetes concerned the seriousness of the
condition, the preventability of progression to diabetes, and the need for
lifestyle change. As well as feedback on the acceptability and use of the
toolkit, four main themes were identified in the data: knowledge and education
needs (of both patients and health professionals); communicating knowledge and
motivating change; redesign of practice systems to support pre-diabetes
management and the role of the health professional. The toolkit we developed was
found to be an acceptable and useful resource for both patients and health
practitioners.
CONCLUSIONS: Three key messages about pre-diabetes were identified. A toolkit of
information materials for patients with pre-diabetes and the health professionals
and ideas for improving practice systems for managing pre-diabetes were developed
and successfully piloted. Further work is needed to establish the best mode of
delivery of the WAKEUP toolkit.
OUTPUT:
| 17403125.txt |
software_name_mentions65 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Structural and functional analysis of cellular networks with CellNetAnalyzer .
BACKGROUND : Mathematical modelling of cellular networks is an integral part of Systems Biology and requires appropriate software tools .
An important class of methods in Systems Biology deals with structural or topological ( parameter - free ) analysis of cellular networks .
So far , software tools providing such methods for both mass - flow ( metabolic ) as well as signal - flow ( signalling and regulatory ) networks are lacking .
RESULTS : Herein we introduce CellNetAnalyzer , a toolbox for MATLAB facilitating , in an interactive and visual manner , a comprehensive structural analysis of metabolic , signalling and regulatory networks .
The particular strengths of CellNetAnalyzer are methods for functional network analysis , i.e. for characterising functional states , for detecting functional dependencies , for identifying intervention strategies , or for giving qualitative predictions on the effects of perturbations .
CellNetAnalyzer extends its predecessor FluxAnalyzer ( originally developed for metabolic network and pathway analysis ) by a new modelling framework for examining signal - flow networks .
Two of the novel methods implemented in CellNetAnalyzer are discussed in more detail regarding algorithmic issues and applications : the computation and analysis ( i ) of shortest positive and shortest negative paths and circuits in interaction graphs and ( ii ) of minimal intervention sets in logical networks .
CONCLUSION : CellNetAnalyzer provides a single suite to perform structural and qualitative analysis of both mass - flow - and signal - flow - based cellular networks in a user - friendly environment .
It provides a large toolbox with various , partially unique , functions and algorithms for functional network analysis .
CellNetAnalyzer is freely available for academic use .
OUTPUT:
| 17408509.txt |
software_name_mentions66 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: A software tool for modification of human voxel models used for application in
radiation protection.
This note describes a new software tool called 'VolumeChange' that was developed
to modify the masses and location of organs of virtual human voxel models. A
voxel model is a three-dimensional representation of the human body in the form
of an array of identification numbers that are arranged in slices, rows and
columns. Each entry in this array represents a voxel; organs are represented by
those voxels having the same identification number. With this tool, two human
voxel models were adjusted to fit the reference organ masses of a male and a
female adult, as defined by the International Commission on Radiological
Protection (ICRP). The alteration of an already existing voxel model is a
complicated process, leading to many problems that have to be solved. To solve
those intricacies in an easy way, a new software tool was developed and is
presented here. If the organs are modified, no bit of tissue, i.e. voxel, may
vanish nor should an extra one appear. That means that organs cannot be modified
without considering the neighbouring tissue. Thus, the principle of organ
modification is based on the reassignment of voxels from one organ/tissue to
another; actually deleting and adding voxels is only possible at the external
surface, i.e. skin. In the software tool described here, the modifications are
done by semi-automatic routines but including human control. Because of the
complexity of the matter, a skilled person has to validate that the applied
changes to organs are anatomically reasonable. A graphical user interface was
designed to fulfil the purpose of a comfortable working process, and an adequate
graphical display of the modified voxel model was developed. Single organs, organ
complexes and even whole limbs can be edited with respect to volume, shape and
location.
OUTPUT:
| 17440236.txt |
software_name_mentions67 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: An R package for analysis of whole - genome association studies .
OBJECTIVE : To provide data classes and methods to facilitate the analysis of whole genome association studies in the R language for statistical computing .
METHODS : We have implemented data classes in which each genotype call is stored as a single byte .
At this density , data for single chromosomes derived from large studies and new high - throughput gene chip platforms can be handled in memory .
We use the object - oriented programming model introduced with version 4 of the S - plus package , usually termed ' S4 methods ' . RESULTS : At the current state of development the package only supports population - based studies , although we would hope to provide support for family - based studies soon .
Both quantitative and qualitative phenotypes may be analysed .
Flexible association testing functions are provided which can carry out single SNP tests which control for potential confounding by quantitative and qualitative covariates .
Tests involving several SNPs taken together as ' tags ' are also supported .
Efficient calculation of pair - wise linkage disequilibrium measures is implemented and data input functions include a function which can download data directly from the international HapMap project website .
OUTPUT:
| 17483596.txt |
software_name_mentions68 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: CALIB : a Bioconductor package for estimating absolute expression levels from two - color microarray data .
UNLABELLED : In this article we describe a new Bioconductor package ' CALIB ' for normalization of two - color microarray data .
This approach is based on the measurements of external controls and estimates an absolute target level for each gene and condition pair , as opposed to working with log - ratios as a relative measure of expression .
Moreover , this method makes no assumptions regarding the distribution of gene expression divergence .
AVAILABILITY : http : / / bioconductor.org / packages / 2.0 / bioc Open Source .
OUTPUT:
| 17485432.txt |
software_name_mentions69 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Genomorama : genome visualization and analysis .
BACKGROUND : The ability to visualize genomic features and design experimental assays that can target specific regions of a genome is essential for modern biology .
To assist in these tasks , we present Genomorama , a software program for interactively displaying multiple genomes and identifying potential DNA hybridization sites for assay design .
RESULTS : Useful features of Genomorama include genome search by DNA hybridization ( probe binding and PCR amplification ) , efficient multi - scale display and manipulation of multiple genomes , support for many genome file types and the ability to search for and retrieve data from the National Center for Biotechnology Information ( NCBI ) Entrez server .
CONCLUSION : Genomorama provides an efficient computational platform for visualizing and analyzing multiple genomes .
OUTPUT:
| 17570856.txt |
software_name_mentions70 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: BiVisu: software tool for bicluster detection and visualization.
BiVisu is an open-source software tool for detecting and visualizing biclusters
embedded in a gene expression matrix. Through the use of appropriate coherence
relations, BiVisu can detect constant, constant-row, constant-column,
additive-related as well as multiplicative-related biclusters. The biclustering
results are then visualized under a 2D setting for easy inspection. In
particular, parallel coordinate (PC) plots for each bicluster are displayed, from
which objective and subjective cluster quality evaluation can be
performed.AVAILABILITY: BiVisu has been developed in Matlab and is available at
http://www.eie.polyu.edu.hk/~nflaw/Biclustering/.
OUTPUT:
| 17586826.txt |
software_name_mentions71 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: GSEA - P : a desktop application for Gene Set Enrichment Analysis .
UNLABELLED : Gene Set Enrichment Analysis ( GSEA ) is a computational method that assesses whether an a priori defined set of genes shows statistically significant , concordant differences between two biological states .
We report the availability of a new version of the Java based software ( GSEA - P 2.0 ) that represents a major improvement on the previous release through the addition of a leading edge analysis component , seamless integration with the Molecular Signature Database ( MSigDB ) and an embedded browser that allows users to search for gene sets and map them to a variety of microarray platform formats .
This functionality makes it possible for users to directly import gene sets from MSigDB for analysis with GSEA .
We have also improved the visualizations in GSEA - P 2.0 and added links to a new form of concise gene set annotations called Gene Set Cards .
These additions , as well as other improvements suggested by over 3500 users who have downloaded the software over the past year have been incorporated into this new release of the GSEA - P Java desktop program .
AVAILABILITY : GSEA - P 2.0 is freely available for academic and commercial users and can be downloaded from http : / / www.broad.mit.edu / GSEA
OUTPUT:
| 17644558.txt |
software_name_mentions72 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: AffyMAPSDetector: a software tool to characterize Affymetrix GeneChip expression
arrays with respect to SNPs.
BACKGROUND: Affymetrix gene expression arrays incorporate paired perfect match
(PM) and mismatch (MM) probes to distinguish true signals from those arising from
cross-hybridization events. A MM signal often shows greater intensity than a PM
signal; we propose that one underlying cause is the presence of allelic variants
arising from single nucleotide polymorphisms (SNPs). To annotate and characterize
SNP contributions to anomalous probe binding behavior we have developed a
software tool called AffyMAPSDetector.
RESULTS: AffyMAPSDetector can be used to describe any Affymetrix expression
GeneChip with respect to SNPs. When AffyMAPSDetector was run on GeneChip
HG-U95Av2 against dbSNP-build-123, we found 7286 probes (belonging to 2,582
probesets) containing SNPs, out of which 325 probes contained at least one SNP at
position 13. Against dbSNP-build-126, 8758 probes (belonging to 3,002 probesets)
contained SNPs, of which 409 probes contained at least one SNP at position 13.
Therefore, depending on the expressed allele, the MM probe can sometimes be the
transcript complement. This information was used to characterize probe
measurements reported in a published, well-replicated lung adenocarcinoma study.
The total intensity distributions showed that the SNP-containing probes had a
larger negative mean intensity difference (PM-MM) and greater range of the
difference than did probes without SNPs. In the sample replicates, SNP-containing
probes with reproducible intensity ratios were identified, allowing selection of
SNP probesets that yielded unique sample signatures. At the gene expression
level, use of the (MM-PM) value for SNP-containing probes resulted in different
Presence/Absence calls for some genes. Such a change in status of the genes has
the clear potential for influencing downstream clustering and classification
results.
CONCLUSION: Output from this tool characterizes SNP-containing probes on GeneChip
microarrays, thus improving our understanding of factors contributing to
expression measurements. The pattern of SNP binding examined so far indicates
distinct behavior of the SNP-containing probes and has the potential to help us
identify new SNPs. Knowing which probes contain SNPs provides flexibility in
determining whether to include or exclude them from gene-expression intensity
calculations; selected sets of SNP-containing probes produce sample-unique
signatures. AffyMAPSDetector information is available at
http://www.binf.gmu.edu/weller/BMC_bioinformatics/AffyMapsDetector/index.html.
OUTPUT:
| 17663786.txt |
software_name_mentions73 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: FRETView : a computer program to simplify the process of obtaining fluorescence resonance energy transfer parameters .
The process of modeling the fluorescence resonance energy transfer ( FRET ) process for a donor - acceptor pair can be rather challenging , yet few computer programs exist that allow such modeling to be done with relative ease .
In order to address this , we have developed a Java - based program , FRETView , which allows numerous FRET parameters to be obtained with just a few mouse clicks .
Being a Java - based program , it runs equally well on all the major operating systems such as Windows , Mac OS X , Linux , Solaris .
The program allows the user to effortlessly input pertinent information about the donor - acceptor pair , including the absorption and / or emission spectra , and outputs the calculated FRET parameters in table format , as well as graphical plots .
OUTPUT:
| 17668122.txt |
software_name_mentions74 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Cicerone : stereotactic neurophysiological recording and deep brain stimulation electrode placement software system .
Stereotactic neurosurgery and neurophysiological microelectrode recordings in both humans and monkeys are typically done with conventional 2D atlases and paper records of the stereotactic coordinates .
This approach is prone to error because the brain size , shape , and location of subcortical structures can vary between subjects .
Furthermore , paper record keeping is inefficient and limits opportunities for data visualization .
To address these limitations , we developed a software tool ( Cicerone ) that enables interactive 3D visualization of co - registered magnetic resonance images ( MRI ) , computed tomography ( CT ) scans , 3D brain atlases , neurophysiological microelectrode recording ( MER ) data , and deep brain stimulation ( DBS ) electrode ( s ) with the volume of tissue activated ( VTA ) as a function of the stimulation parameters .
The software can be used in pre - operative planning to help select the optimal position on the skull for burr hole ( in humans ) or chamber ( in monkeys ) placement to maximize the likelihood of complete microelectrode and DBS coverage of the intended anatomical target .
Intra - operatively , Cicerone allows entry of the stereotactic microdrive coordinates and MER data , enabling real - time interactive visualization of the electrode location in 3D relative to the surrounding neuroanatomy and neurophysiology .
In addition , the software enables prediction of the VTA generated by DBS for a range of electrode trajectories and tip locations .
In turn , the neurosurgeon can use the combination of anatomical ( MRI / CT / 3D brain atlas ) , neurophysiological ( MER ) , and electrical ( DBS VTA ) data to optimize the placement of the DBS electrode prior to permanent implantation .
OUTPUT:
| 17691348.txt |
software_name_mentions75 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: A useful programme in BASIC for axonal morphometry with introduction of new cytoskeletal parameters .
Interest in the structure of axons and quantification of their components has been growing over the last years .
However , the existing literature contains few reports of available computer programmes to facilitate such studies .
This paper presents a fully comprehensive BASIC programme for the morphometric analysis of electron micrographs of cross - sectional nerve fibres .
From drawings of fibre and axonal contours and dots of the microtubules and neurofilaments , the programme calculates the following parameters : area , diameter and form factor of the fibres and axons , number and density of microtubules and neurofilaments , proportion between microtubules and neurofilaments ( R - proportion ) , myelin thickness and the diameter of the axon relative to its sheath ( g - ratio ) . The programme also introduces three new parameters to analyse the degree of uniformity of microtubule and neurofilament distribution : distances between microtubules and between neurofilaments , equilateral index and cytoskeletal intermingling index .
The programme is written in Microsoft BASIC Interpreter for Apple Macintosh ( Microsoft Corporation ) but can be used on other computers .
Although the programme has been tested on adult rat optic nerve fibres , it can be used for different projects concerning axonal morphometry .
OUTPUT:
| 1787747.txt |
software_name_mentions76 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Signal - 3 L : A 3 - layer approach for predicting signal peptides .
Functioning as an " address tag " that directs nascent proteins to their proper cellular and extracellular locations , signal peptides have become a crucial tool in finding new drugs or reprogramming cells for gene therapy .
To effectively and timely use such a tool , however , the first important thing is to develop an automated method for rapidly and accurately identifying the signal peptide for a given nascent protein .
With the avalanche of new protein sequences generated in the post - genomic era , the challenge has become even more urgent and critical .
In this paper , we have developed a novel method for predicting signal peptide sequences and their cleavage sites in human , plant , animal , eukaryotic , Gram - positive , and Gram - negative protein sequences , respectively .
The new predictor is called Signal - 3 L that consists of three prediction engines working , respectively , for the following three progressively deepening layers : ( 1 ) identifying a query protein as secretory or non - secretory by an ensemble classifier formed by fusing many individual OET - KNN ( optimized evidence - theoretic K nearest neighbor ) classifiers operated in various dimensions of PseAA ( pseudo amino acid ) composition spaces ; ( 2 ) selecting a set of candidates for the possible signal peptide cleavage sites of a query secretory protein by a subsite - coupled discrimination algorithm ; ( 3 ) determining the final cleavage site by fusing the global sequence alignment outcome for each of the aforementioned candidates through a voting system .
Signal - 3 L is featured by high success prediction rates with short computational time , and hence is particularly useful for the analysis of large - scale datasets .
Signal - 3 L is freely available as a web - server at http : / / chou.med.harvard.edu / bioinf / Signal - 3 L / or http : / / 202.120.37.186 / bioinf / Signal - 3 L , where , to further support the demand of the related areas , the signal peptides identified by Signal - 3 L for all the protein entries in Swiss - Prot databank that do not have signal peptide annotations or are annotated with uncertain terms but are classified by Signal - 3 L as secretory proteins are provided in a downloadable file .
The large - scale file is prepared with Microsoft Excel and named " Tab - Signal - 3L.xls " , and will be updated once a year to include new protein entries and reflect the continuous development of Signal - 3L .
OUTPUT:
| 17880924.txt |
software_name_mentions77 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Integration of biological networks and gene expression data using Cytoscape .
Cytoscape is a free software package for visualizing , modeling and analyzing molecular and genetic interaction networks .
This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling , and other functional genomics and proteomics experiments , in the context of an interaction network obtained for genes of interest .
Five major steps are described : ( i ) obtaining a gene or protein network , ( ii ) displaying the network using layout algorithms , ( iii ) integrating with gene expression and other functional attributes , ( iv ) identifying putative complexes and functional modules and ( v ) identifying enriched Gene Ontology annotations in the network .
These steps provide a broad sample of the types of analyses performed by Cytoscape .
OUTPUT:
| 17947979.txt |
software_name_mentions78 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Ensemble : a web - based system for psychology survey and experiment management .
We provide a description of Ensemble , a suite of Web - integrated modules for managing and analyzing data associated with psychology experiments in a small research lab .
The system delivers interfaces via a Web browser for creating and presenting simple surveys without the need to author Web pages and with little or no programming effort .
The surveys may be extended by selecting and presenting auditory and / or visual stimuli with MATLAB and Flash to enable a wide range of psychophysical and cognitive experiments which do not require the recording of precise reaction times .
Additionally , one is provided with the ability to administer and present experiments remotely .
The software technologies employed by the various modules of Ensemble are MySQL , PHP , MATLAB , and Flash .
The code for Ensemble is open source and available to the public , so that its functions can be readily extended by users .
We describe the architecture of the system , the functionality of each module , and provide basic examples of the interfaces .
OUTPUT:
| 17958178.txt |
software_name_mentions79 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: A web - based information system for management and analysis of patient data after refractive eye surgery .
The aim was to develop a software tool for refractive surgeons using a standard user - friendly web - based interface , providing the user with a secure environment to protect large volumes of patient data .
The software application was named " Internet - based refractive analysis " ( IBRA ) , and was programmed with the computer languages PHP , HTML and JavaScript , attached to the opensource MySQL database .
IBRA facilitated internationally accepted presentation methods including the stability chart , the predictability chart and the safety chart ; it was able to perform vector analysis for the course of a single patient or for group data .
With the integrated nomogram calculation , treatment could be customised to reduce the postoperative refractive error .
Multicenter functions permitted quality - control comparisons between different surgeons and laser units .
OUTPUT:
| 17964683.txt |
software_name_mentions80 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: PET monitoring of cancer therapy with 3He and 12C beams: a study with the GEANT4
toolkit.
We study the spatial distributions of beta(+)-activity produced by therapeutic
beams of (3)He and (12)C ions in various tissue-like materials. The calculations
were performed within a Monte Carlo model for heavy-ion therapy (MCHIT) based on
the GEANT4 toolkit. The contributions from positron-emitting nuclei with T(1/2) >
10 s, namely (10,11)C, (13)N, (14,15)O, (17,18)F and (30)P, were calculated and
compared with experimental data obtained during and after irradiation, where
available. Positron-emitting nuclei are created by a (12)C beam in fragmentation
reactions of projectile and target nuclei. This leads to a beta(+)-activity
profile characterized by a noticeable peak located close to the Bragg peak in the
corresponding depth-dose distribution. This can be used for dose monitoring in
carbon-ion therapy of cancer. In contrast, as most of the positron-emitting
nuclei are produced by a (3)He beam in target fragmentation reactions, the
calculated total beta(+)-activity during or soon after the irradiation period is
evenly distributed within the projectile range. However, we predict also the
presence of (13)N, (14)O, (17,18)F created in charge-transfer reactions by
low-energy (3)He ions close to the end of their range in several tissue-like
media. The time evolution of beta(+)-activity profiles was investigated for both
kinds of beams. We found that due to the production of (18)F nuclides the
beta(+)-activity profile measured 2 or 3 h after irradiation with (3)He ions will
have a distinct peak correlated with the maximum of depth-dose distribution. We
also found certain advantages of low-energy (3)He beams over low-energy proton
beams for reliable PET monitoring during particle therapy of shallow-located
tumours. In this case the distal edge of beta(+)-activity distribution from (17)F
nuclei clearly marks the range of (3)He in tissues.
OUTPUT:
| 18065840.txt |
software_name_mentions81 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: The GEANT4 toolkit for microdosimetry calculations: application to microbeam
radiation therapy (MRT).
Theoretical dose distributions for microbeam radiation therapy (MRT) are computed
in this paper using the GEANT4 Monte Carlo (MC) simulation toolkit. MRT is an
innovative experimental radiotherapy technique carried out using an array of
parallel microbeams of synchrotron-wiggler-generated x rays. Although the
biological mechanisms underlying the effects of microbeams are still largely
unknown, the effectiveness of MRT can be traced back to the natural ability of
normal tissues to rapidly repair small damages to the vasculature, and on the
lack of a similar healing process in tumoral tissues. Contrary to conventional
therapy, in which each beam is at least several millimeters wide, the narrowness
of the microbeams allows a rapid regeneration of the blood vessels along the
beams' trajectories. For this reason the calculation of the "valley" dose is of
crucial importance and the correct use of MC codes for such purposes must be
understood. GEANT4 offers, in addition to the standard libraries, a specialized
package specifically designed to deal with electromagnetic interactions of
particles with matter for energies down to 250 eV. This package implements two
different approaches for electron and photon transport, one based on evaluated
data libraries, the other adopting analytical models. These features are
exploited to cross-check theoretical computations for MRT. The lateral and depth
dose profiles are studied for the irradiation of a 20 cm diameter, 20 cm long
cylindrical phantom, with cylindrical sources of different size and energy.
Microbeam arrays are simulated with the aid of superposition algorithms, and the
ratios of peak-to-valley doses are computed for typical cases used in preclinical
assays. Dose profiles obtained using the GEANT4 evaluated data libraries and
analytical models are compared with simulation results previously obtained using
the PENELOPE code. The results show that dose profiles computed with GEANT4's
analytical model are almost indistinguishable from those obtained with the
PENELOPE code, but some noticeable differences appear when the evaluated data
libraries are used.
OUTPUT:
| 18072497.txt |
software_name_mentions82 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Implementing the Medical Desktop : tools for the integration of independent information resources .
The increasing availability of medical information resources has moved the " Medical Desktop " from a theoretical construct to a practical necessity .
Many micro - computers are becoming available in clinical and academic settings that can access several medical information applications .
These computers are usually not powerful workstations that are part of a clinically oriented information support system , but are personal computers with varied capabilities .
The applications on these computers come from different sources , are accessed through different user interfaces and do not share data well .
The de facto " Medical Desktop " this situation presents will discourage most end - users because the combination of applications is complex , the applications are poorly integrated , and individual applications are inconsistent .
At the State University of New York at Buffalo School of Medicine and Biomedical Sciences we have developed several Microsoft Windows - based tools that accept a systems level diversity of resources , but work toward the construction of a coherent " Medical Desktop .
" These tools include a lexical term linker , a resource database , and a context sensitive help system that is tailored to locally available resources .
OUTPUT:
| 1807667.txt |
software_name_mentions83 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Using heteroskedasticity - consistent standard error estimators in OLS regression : an introduction and software implementation .
Homoskedasticity is an important assumption in ordinary least squares ( OLS ) regression .
Although the estimator of the regression parameters in OLS regression is unbiased when the homoskedasticity assumption is violated , the estimator of the covariance matrix of the parameter estimates can be biased and inconsistent under heteroskedasticity , which can produce significance tests and confidence intervals that can be liberal or conservative .
After a brief description of heteroskedasticity and its effects on inference in OLS regression , we discuss a family of heteroskedasticity - consistent standard error estimators for OLS regression and argue investigators should routinely use one of these estimators when conducting hypothesis tests using OLS regression .
To facilitate the adoption of this recommendation , we provide easy - to - use SPSS and SAS macros to implement the procedures discussed here .
OUTPUT:
| 18183883.txt |
software_name_mentions84 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: LTRharvest , an efficient and flexible software for de novo detection of LTR retrotransposons .
BACKGROUND : Transposable elements are abundant in eukaryotic genomes and it is believed that they have a significant impact on the evolution of gene and chromosome structure .
While there are several completed eukaryotic genome projects , there are only few high quality genome wide annotations of transposable elements .
Therefore , there is a considerable demand for computational identification of transposable elements .
LTR retrotransposons , an important subclass of transposable elements , are well suited for computational identification , as they contain long terminal repeats ( LTRs ) . RESULTS : We have developed a software tool LTRharvest for the de novo detection of full length LTR retrotransposons in large sequence sets .
LTRharvest efficiently delivers high quality annotations based on known LTR transposon features like length , distance , and sequence motifs .
A quality validation of LTRharvest against a gold standard annotation for Saccharomyces cerevisae and Drosophila melanogaster shows a sensitivity of up to 90 % and 97 % and specificity of 100 % and 72 % , respectively .
This is comparable or slightly better than annotations for previous software tools .
The main advantage of LTRharvest over previous tools is ( a ) its ability to efficiently handle large datasets from finished or unfinished genome projects , ( b ) its flexibility in incorporating known sequence features into the prediction , and ( c ) its availability as an open source software .
CONCLUSION : LTRharvest is an efficient software tool delivering high quality annotation of LTR retrotransposons .
It can , for example , process the largest human chromosome in approx .
8 minutes on a Linux PC with 4 GB of memory .
Its flexibility and small space and run - time requirements makes LTRharvest a very competitive candidate for future LTR retrotransposon annotation projects .
Moreover , the structured design and implementation and the availability as open source provides an excellent base for incorporating novel concepts to further improve prediction of LTR retrotransposons .
OUTPUT:
| 18194517.txt |
software_name_mentions85 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: PGMapper : a web - based tool linking phenotype to genes .
SUMMARY : With the availability of whole genome sequence in many species , linkage analysis , positional cloning and microarray are gradually becoming powerful tools for investigating the links between phenotype and genotype or genes .
However , in these methods , causative genes underlying a quantitative trait locus , or a disease , are usually located within a large genomic region or a large set of genes .
Examining the function of every gene is very time consuming and needs to retrieve and integrate the information from multiple databases or genome resources .
PGMapper is a software tool for automatically matching phenotype to genes from a defined genome region or a group of given genes by combining the mapping information from the Ensembl database and gene function information from the OMIM and PubMed databases .
PGMapper is currently available for candidate gene search of human , mouse , rat , zebrafish and 12 other species .
AVAILABILITY : Available online at http : / / www.genediscovery.org / pgmapper / index.jsp.
OUTPUT:
| 18204061.txt |
software_name_mentions86 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: SGDI : system for genomic data integration .
This paper describes a framework for collecting , annotating , and archiving high - throughput assays from multiple experiments conducted on one or more series of samples .
Specific applications include support for large - scale surveys of related transcriptional profiling studies , for investigations of the genetics of gene expression and for joint analysis of copy number variation and mRNA abundance .
Our approach consists of data capture and modeling processes rooted in R / Bioconductor , sample annotation and sequence constituent ontology management based in R , secure data archiving in PostgreSQL , and browser - based workspace creation and management rooted in Zope .
This effort has generated a completely transparent , extensible , and customizable interface to large archives of high - throughput assays .
Sources and prototype interfaces are accessible at www.sgdi.org / software .
OUTPUT:
| 18229682.txt |
software_name_mentions87 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: The Clinical Outcomes Assessment Toolkit: a framework to support automated
clinical records-based outcomes assessment and performance measurement research.
The Clinical Outcomes Assessment Toolkit (COAT) was created through a
collaboration between the University of California, Los Angeles and Brigham and
Women's Hospital to address the challenge of gathering, formatting, and
abstracting data for clinical outcomes and performance measurement research. COAT
provides a framework for the development of information pipelines to transform
clinical data from its original structured, semi-structured, and unstructured
forms to a standardized format amenable to statistical analysis. This system
includes a collection of clinical data structures, reusable utilities for
information analysis and transformation, and a graphical user interface through
which pipelines can be controlled and their results audited by nontechnical
users. The COAT architecture is presented, as well as two case studies of current
implementations in the domain of prostate cancer outcomes assessment.
OUTPUT:
| 18308990.txt |
software_name_mentions88 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Optimizations for the EcoPod field identification tool .
BACKGROUND : We sketch our species identification tool for palm sized computers that helps knowledgeable observers with census activities .
An algorithm turns an identification matrix into a minimal length series of questions that guide the operator towards identification .
Historic observation data from the census geographic area helps minimize question volume .
We explore how much historic data is required to boost performance , and whether the use of history negatively impacts identification of rare species .
We also explore how characteristics of the matrix interact with the algorithm , and how best to predict the probability of observing a previously unseen species .
RESULTS : Point counts of birds taken at Stanford University ' s Jasper Ridge Biological Preserve between 2000 and 2005 were used to examine the algorithm .
A computer identified species by correctly answering , and counting the algorithm ' s questions .
We also explored how the character density of the key matrix and the theoretical minimum number of questions for each bird in the matrix influenced the algorithm .
Our investigation of the required probability smoothing determined whether Laplace smoothing of observation probabilities was sufficient , or whether the more complex Good - Turing technique is required .
CONCLUSION : Historic data improved identification speed , but only impacted the top 25 % most frequently observed birds .
For rare birds the history based algorithms did not impose a noticeable penalty in the number of questions required for identification .
For our dataset neither age of the historic data , nor the number of observation years impacted the algorithm .
Density of characters for different taxa in the identification matrix did not impact the algorithms .
Intrinsic differences in identifying different birds did affect the algorithm , but the differences affected the baseline method of not using historic data to exactly the same degree .
We found that Laplace smoothing performed better for rare species than Simple Good - Turing , and that , contrary to expectation , the technique did not then adversely affect identification performance for frequently observed birds .
OUTPUT:
| 18366649.txt |
software_name_mentions89 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: JCoast - a biologist-centric software tool for data mining and comparison of
prokaryotic (meta)genomes.
BACKGROUND: Current sequencing technologies give access to sequence information
for genomes and metagenomes at a tremendous speed. Subsequent data processing is
mainly performed by automatic pipelines provided by the sequencing centers.
Although, standardised workflows are desirable and useful in many respects,
rational data mining, comparative genomics, and especially the interpretation of
the sequence information in the biological context, demands for intuitive,
flexible, and extendable solutions.
RESULTS: The JCoast software tool was primarily designed to analyse and compare
(meta)genome sequences of prokaryotes. Based on a pre-computed GenDB database
project, JCoast offers a flexible graphical user interface (GUI), as well as an
application programming interface (API) that facilitates back-end data access.
JCoast offers individual, cross genome-, and metagenome analysis, and assists the
biologist in exploration of large and complex datasets.
CONCLUSION: JCoast combines all functions required for the mining, annotation,
and interpretation of (meta)genomic data. The lightweight software solution
allows the user to easily take advantage of advanced back-end database structures
by providing a programming and graphical user interface to answer biological
questions. JCoast is available at the project homepage.
OUTPUT:
| 18380896.txt |
software_name_mentions90 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: jModelTest : phylogenetic model averaging .
jModelTest is a new program for the statistical selection of models of nucleotide substitution based on " Phyml " ( Guindon and Gascuel 2003. A simple , fast , and accurate algorithm to estimate large phylogenies by maximum likelihood .
Syst Biol. 52 : 696 - 704. ) . It implements 5 different selection strategies , including " hierarchical and dynamical likelihood ratio tests , " the " Akaike information criterion , " the " Bayesian information criterion , " and a " decision - theoretic performance - based " approach .
This program also calculates the relative importance and model - averaged estimates of substitution parameters , including a model - averaged estimate of the phylogeny .
jModelTest is written in Java and runs under Mac OSX , Windows , and Unix systems with a Java Runtime Environment installed .
The program , including documentation , can be freely downloaded from the software section at http : / / darwin.uvigo.es.
OUTPUT:
| 18397919.txt |
software_name_mentions91 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Phylogeny.fr : robust phylogenetic analysis for the non - specialist .
Phylogenetic analyses are central to many research areas in biology and typically involve the identification of homologous sequences , their multiple alignment , the phylogenetic reconstruction and the graphical representation of the inferred tree .
The Phylogeny.fr platform transparently chains programs to automatically perform these tasks .
It is primarily designed for biologists with no experience in phylogeny , but can also meet the needs of specialists ; the first ones will find up - to - date tools chained in a phylogeny pipeline to analyze their data in a simple and robust way , while the specialists will be able to easily build and run sophisticated analyses .
Phylogeny.fr offers three main modes .
The ' One Click ' mode targets non - specialists and provides a ready - to - use pipeline chaining programs with recognized accuracy and speed : MUSCLE for multiple alignment , PhyML for tree building , and TreeDyn for tree rendering .
All parameters are set up to suit most studies , and users only have to provide their input sequences to obtain a ready - to - print tree .
The ' Advanced ' mode uses the same pipeline but allows the parameters of each program to be customized by users .
The ' A la Carte ' mode offers more flexibility and sophistication , as users can build their own pipeline by selecting and setting up the required steps from a large choice of tools to suit their specific needs .
Prior to phylogenetic analysis , users can also collect neighbors of a query sequence by running BLAST on general or specialized databases .
A guide tree then helps to select neighbor sequences to be used as input for the phylogeny pipeline .
Phylogeny.fr is available at : http : / / www.phylogeny.fr /
OUTPUT:
| 18424797.txt |
software_name_mentions92 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Loading and preparing data for analysis in spotfire .
This unit strictly focuses on data preparation within Spotfire .
Microarray data exist in a variety of formats , which often depend on the particular array technology and detection instruments used .
The first protocols in this unit describe loading Affymetrix and GenePix data into Spotfire .
Once the data are loaded , it is necessary to filter and preprocess the data prior to analysis .
Subsequently , the data transformation and normalization techniques presented here , are critical to correctly performing powerful microarray data mining expeditions .
These steps extract or enhance meaningful data characteristics and prepare the data for the application of certain analysis methods such as statistical tests to compute significance and clustering methods - which mostly require data to be normally distributed .
The unit outlines several methods for normalizing the data within an experiment and between multiple experiments .
OUTPUT:
| 18428734.txt |
software_name_mentions93 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Efficient computation of three - dimensional protein structures in solution from nuclear magnetic resonance data using the program DIANA and the supporting programs CALIBA , HABAS and GLOMSA .
A novel procedure for efficient computation of three - dimensional protein structures from nuclear magnetic resonance ( n.m.r. ) data in solution is described , which is based on using the program DIANA in combination with the supporting programs CALIBA , HABAS and GLOMSA .
The first part of this paper describes the new programs DIANA .
CALIBA and GLOMSA .
DIANA is a new , fully vectorized implementation of the variable target function algorithm for the computation of protein structures from n.m.r. data .
Its main advantages , when compared to previously available programs using the variable target function algorithm , are a significant reduction of the computation time , and a novel treatment of experimental distance constraints involving diastereotopic groups of hydrogen atoms that were not individually assigned .
CALIBA converts the measured nuclear Overhauser effects into upper distance limits and thus prepares the input for the previously described program HABAS and for DIANA .
GLOMSA is used for obtaining individual assignments for pairs of diastereotopic substituents by comparison of the experimental constraints with preliminary results of the structure calculations .
With its general outlay , the presently used combination of the four programs is particularly user - friendly .
In the second part of the paper , initial results are presented on the influence of the novel DIANA treatment of diastereotopic protons on the quality of the structures obtained , and a systematic study of the central processing unit times needed for the same protein structure calculation on a range of different , commonly available computers is described .
OUTPUT:
| 1847217.txt |
software_name_mentions94 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Fill My Datebook: a software tool to generate and handle lists of events.
Electronic calendars, and especially Internet-based calendars, are becoming more
and more popular. Their advantages over paper calendars include being able to
easily share events with others, gain remote access, organize multiple calendars,
and receive visible and audible reminders. Scientific experiments often include a
huge number of events that have to be organized. Experimental schedules that
follow a fixed scheme can be described as lists of events. The software
application presented here allows for the easy generation, management, and
storage of lists of events using the Internet-based application Google Calendar.
OUTPUT:
| 18522047.txt |
software_name_mentions95 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: StAR: a simple tool for the statistical comparison of ROC curves.
BACKGROUND: As in many different areas of science and technology, most important
problems in bioinformatics rely on the proper development and assessment of
binary classifiers. A generalized assessment of the performance of binary
classifiers is typically carried out through the analysis of their receiver
operating characteristic (ROC) curves. The area under the ROC curve (AUC)
constitutes a popular indicator of the performance of a binary classifier.
However, the assessment of the statistical significance of the difference between
any two classifiers based on this measure is not a straightforward task, since
not many freely available tools exist. Most existing software is either not free,
difficult to use or not easy to automate when a comparative assessment of the
performance of many binary classifiers is intended. This constitutes the typical
scenario for the optimization of parameters when developing new classifiers and
also for their performance validation through the comparison to previous art.
RESULTS: In this work we describe and release new software to assess the
statistical significance of the observed difference between the AUCs of any two
classifiers for a common task estimated from paired data or unpaired balanced
data. The software is able to perform a pairwise comparison of many classifiers
in a single run, without requiring any expert or advanced knowledge to use it.
The software relies on a non-parametric test for the difference of the AUCs that
accounts for the correlation of the ROC curves. The results are displayed
graphically and can be easily customized by the user. A human-readable report is
generated and the complete data resulting from the analysis are also available
for download, which can be used for further analysis with other software. The
software is released as a web server that can be used in any client platform and
also as a standalone application for the Linux operating system.
CONCLUSION: A new software for the statistical comparison of ROC curves is
released here as a web server and also as standalone software for the LINUX
operating system.
OUTPUT:
| 18534022.txt |
software_name_mentions96 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Application of the Linux cluster for exhaustive window haplotype analysis using the FBAT and Unphased programs .
BACKGROUND : Genetic association studies have been used to map disease - causing genes .
A newly introduced statistical method , called exhaustive haplotype association study , analyzes genetic information consisting of different numbers and combinations of DNA sequence variations along a chromosome .
Such studies involve a large number of statistical calculations and subsequently high computing power .
It is possible to develop parallel algorithms and codes to perform the calculations on a high performance computing ( HPC ) system .
However , most existing commonly - used statistic packages for genetic studies are non - parallel versions .
Alternatively , one may use the cutting - edge technology of grid computing and its packages to conduct non - parallel genetic statistical packages on a centralized HPC system or distributed computing systems .
In this paper , we report the utilization of a queuing scheduler built on the Grid Engine and run on a Rocks Linux cluster for our genetic statistical studies .
RESULTS : Analysis of both consecutive and combinational window haplotypes was conducted by the FBAT ( Laird et al. , 2000 ) and Unphased ( Dudbridge , 2003 ) programs .
The dataset consisted of 26 loci from 277 extended families ( 1484 persons ) . Using the Rocks Linux cluster with 22 compute - nodes , FBAT jobs performed about 14.4 - 15.9 times faster , while Unphased jobs performed 1.1 - 18.6 times faster compared to the accumulated computation duration .
CONCLUSION : Execution of exhaustive haplotype analysis using non - parallel software packages on a Linux - based system is an effective and efficient approach in terms of cost and performance .
OUTPUT:
| 18541045.txt |
software_name_mentions97 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Numbat : an interactive software tool for fitting Deltachi - tensors to molecular coordinates using pseudocontact shifts .
Pseudocontact shift ( PCS ) effects induced by a paramagnetic lanthanide bound to a protein have become increasingly popular in NMR spectroscopy as they yield a complementary set of orientational and long - range structural restraints .
PCS are a manifestation of the chi - tensor anisotropy , the Deltachi - tensor , which in turn can be determined from the PCS .
Once the Deltachi - tensor has been determined , PCS become powerful long - range restraints for the study of protein structure and protein - ligand complexes .
Here we present the newly developed package Numbat ( New User - friendly Method Built for Automatic Deltachi - Tensor determination ) . With a Graphical User Interface ( GUI ) that allows a high degree of interactivity , Numbat is specifically designed for the computation of the complete set of Deltachi - tensor parameters ( including shape , location and orientation with respect to the protein ) from a set of experimentally measured PCS and the protein structure coordinates .
Use of the program for Linux and Windows operating systems is illustrated by building a model of the complex between the E .
coli DNA polymerase III subunits epsilon186 and theta using PCS .
OUTPUT:
| 18574699.txt |
software_name_mentions98 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: The Systems Biology Research Tool: evolvable open-source software.
BACKGROUND: Research in the field of systems biology requires software for a
variety of purposes. Software must be used to store, retrieve, analyze, and
sometimes even to collect the data obtained from system-level (often
high-throughput) experiments. Software must also be used to implement
mathematical models and algorithms required for simulation and theoretical
predictions on the system-level.
RESULTS: We introduce a free, easy-to-use, open-source, integrated software
platform called the Systems Biology Research Tool (SBRT) to facilitate the
computational aspects of systems biology. The SBRT currently performs 35 methods
for analyzing stoichiometric networks and 16 methods from fields such as graph
theory, geometry, algebra, and combinatorics. New computational techniques can be
added to the SBRT via process plug-ins, providing a high degree of evolvability
and a unifying framework for software development in systems biology.
CONCLUSION: The Systems Biology Research Tool represents a technological advance
for systems biology. This software can be used to make sophisticated
computational techniques accessible to everyone (including those with no
programming ability), to facilitate cooperation among researchers, and to
expedite progress in the field of systems biology.
OUTPUT:
| 18588708.txt |
software_name_mentions99 | Instruction: Given an abstract identity all software names from it by highlighting within <mark> and </mark> tags. Also, apply these guidelines to help with accuracy:
1.Longest noun phrase where one explicit software name is the head noun should be marked
2.If the noun phrase contains more than one explicit software name, the explicit software names should be marked individually
3.Software name tagging should NOT include the description or content of software
4.Full software name and its following acronym within parenthesis should be marked as a whole
5.Acronym type of software name and its following full name within parenthesis should be marked as a whole
6.Software name and its following version number should be marked as a whole
INPUT: Synchronous versus asynchronous modeling of gene regulatory networks .
MOTIVATION : In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems .
This has been further facilitated by the increasing availability of experimental data on gene - gene , protein - protein and gene - protein interactions .
The two dynamical properties that are often experimentally testable are perturbations and stable steady states .
Although a lot of work has been done on the identification of steady states , not much work has been reported on in silico modeling of cellular differentiation processes .
RESULTS : In this manuscript , we provide algorithms based on reduced ordered binary decision diagrams ( ROBDDs ) for Boolean modeling of gene regulatory networks .
Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed .
These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software .
Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols , and their effect on cell differentiation processes .
These algorithms were validated on the T - helper model showing the correct steady state identification and Th1 - Th2 cellular differentiation process .
AVAILABILITY : The software binaries for Windows and Linux platforms can be downloaded from http : / / si2.epfl.ch / ~ garg / genysis.html.
OUTPUT:
| 18614585.txt |