bluuebunny/crossref_metadata_2025_split huggingface dataset:
RAD and the RAD Study-Annotator: an approach to collection, organization and exchange of all relevant information for high-throughput gene expression studies
Authors: Manduchi E. , Grant G. R. , He H. , Liu J. , Mailman M. D. , Pizarro A. D. , Whetzel P. L. , Stoeckert C. J. 
Published: February 2004

Abstract
Motivation: Gene expression array technology has become increasingly widespread among researchers who recognize its numerous promises. At the same time, bench biologists and bioinformaticians have come to appreciate increasingly the importance of establishing a collaborative dialog from the onset of a study and of collecting and exchanging detailed information on the many experimental and computational procedures using a structured mechanism. This is crucial for adequate analyses of this kind of data.
Results: The RNA Abundance Database (RAD; http://www.cbil.upenn.edu/RAD) provides a comprehensive MIAME-supportive infrastructure for gene expression data management and makes extensive use of ontologies. Specific details on protocols, biomaterials, study designs, etc. are collected through a user-friendly suite of web annotation forms. Software has been developed to generate MAGE-ML documents to enable easy export of studies stored in RAD to any other database accepting data in this format (e.g. ArrayExpress). RAD is part of a more general Genomics Unified Schema (http://www.gusdb.org), which includes a richly annotated gene index (http://www.allgenes.org), thus providing a platform that integrates genomic and transcriptomic data from multiple organisms. This infrastructure enables a large variety of queries that incorporate visualization and analysis tools and have been tailored to serve the specific needs of projects focusing on particular organisms or biological systems.
Availability: The system is freely available. Information on how to obtain it and how to install it can be found at http://www.cbil.upenn.edu/RAD/RAD-installation.htm
Rapid and sensitive dot-matrix methods for genome analysis
Authors: Huang Yue , Zhang Ling 
Published: February 2004

Abstract
Motivation: Dot-matrix plots are widely used for similarity analysis of biological sequences. Many algorithms and computer software tools have been developed for this purpose. Though some of these tools have been reported to handle sequences of a few 100 kb, analysis of genome sequences with a length of &gt;10 Mb on a microcomputer is still impractical due to long execution time and computer memory requirement.
Results: Two dot-matrix comparison methods have been developed for analysis of large sequences. The methods initially locate similarity regions between two sequences using a fast word search algorithm, followed with an explicit comparison on these regions. Since the initial screening removes most of random matches, the computing time is substantially reduced. The methods produce high quality dot-matrix plots with low background noise. Space requirements are linear, so the algorithms can be used for comparison of genome size sequences. Computing speed may be affected by highly repetitive sequence structures of eukaryote genomes. A dot-matrix plot of Yeast genome (12 Mb) with both strands was generated in 80 s with a 1 GHz personal computer.
Availability: The implementation of the described methods in C language is available at http://www.lynnon.com/dotplot/index.html
Mismatch string kernels for discriminative protein classification
Authors: Leslie Christina S. , Eskin Eleazar , Cohen Adiel , Weston Jason , Noble William Stafford 
Published: February 2004

Abstract
Motivation: Classification of proteins sequences into functional and structural families based on sequence homology is a central problem in computational biology. Discriminative supervised machine learning approaches provide good performance, but simplicity and computational efficiency of training and prediction are also important concerns.
Results: We introduce a class of string kernels, called mismatch kernels, for use with support vector machines (SVMs) in a discriminative approach to the problem of protein classification and remote homology detection. These kernels measure sequence similarity based on shared occurrences of fixed-length patterns in the data, allowing for mutations between patterns. Thus, the kernels provide a biologically well-motivated way to compare protein sequences without relying on family-based generative models such as hidden Markov models. We compute the kernels efficiently using a mismatch tree data structure, allowing us to calculate the contributions of all patterns occurring in the data in one pass while traversing the tree. When used with an SVM, the kernels enable fast prediction on test sequences. We report experiments on two benchmark SCOP datasets, where we show that the mismatch kernel used with an SVM classifier performs competitively with state-of-the-art methods for homology detection, particularly when very few training examples are available. Examination of the highest-weighted patterns learned by the SVM classifier recovers biologically important motifs in protein families and superfamilies.
Availability: SVM software is publicly available at http://microarray.cpmc.columbia.edu/gist. Mismatch kernel software is available upon request.
Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural information
Authors: Ahmad Shandar , Gromiha M. Michael , Sarai Akinori 
Published: February 2004

Abstract
Motivation: Though vitally important to cell function, the mechanism of protein–DNA binding has not yet been completely understood. We therefore analysed the relationship between DNA binding and protein sequence composition, solvent accessibility and secondary structure. Using non-redundant databases of transcription factors and protein–DNA complexes, neural network models were developed to utilize the information present in this relationship to predict DNA-binding proteins and their binding residues.
Results: Sequence composition was found to provide sufficient information to predict the probability of its binding to DNA with nearly 69% sensitivity at 64% accuracy for the considered proteins; sequence neighbourhood and solvent accessibility information were sufficient to make binding site predictions with 40% sensitivity at 79% accuracy. Detailed analysis of binding residues shows that some three- and five-residue segments frequently bind to DNA and that solvent accessibility plays a major role in binding. Although, binding behaviour was not associated with any particular secondary structure, there were interesting exceptions at the residue level. Over-representation of some residues in the binding sites was largely lost at the total sequence level, but a different kind of compositional preference was observed in DNA-binding proteins.
Availability: Online predictions of DNA-binding proteins and binding sites are available at http://www.netasa.org/dbs-pred/
Inverse modeling using multi-block PLS to determine the environmental conditions that provide optimal cellular function
Authors: Hwang Daehee , Stephanopoulos George , Chan Christina 
Published: February 2004

Abstract
Motivations: Tissue engineering constitutes an important field with its potential of addressing the current shortage in organ availability. To successfully develop tissue-engineered organs, it is crucial to understand how to maintain the cells under conditions that maximize their ability to perform their physiological roles, regardless of the environment, whether the cells are part of an extracorporeal system, such as the bioartificial liver assist device, or an implantable tissue-engineered device. Our goals are to (1) provide insight into how cells will behave when confronted with changes in its environment and (2) determine the optimal environmental factors to achieve a desired level of cellular function.
Results: Diverse sets of environmental factors were used to systematically perturb the metabolic behavior associated with pre-conditioning and plasma supplementation. To probe metabolic state of hepatocytes, metabolic flux analysis was used to obtain the metabolic profile. We applied a multi-block partial least square (MPLS) model to relate environmental factors and fluxes to levels of intracellular lipids and urea synthesis. The MPLS model identified: (1) the most influential environmental factors and (2) how the metabolic pathways are altered by these factors. Finally, we inverted the MPLS model to determine the concentrations and types of environmental factors required to obtain the most economical solution for achieving optimal levels of cellular function for practical situations.
Supplementary information: Appendices can be accessed at http://www.chems.msu.edu/fac.pub/chan/MPLS_App.pdf
Comparison of Li–Wong and loglinear mixed models for the statistical analysis of oligonucleotide arrays
Authors: Chu Tzu-Ming , Weir B.S. , Wolfinger Russell D. 
Published: February 2004

Abstract
Motivation: Li and Wong have described some useful statistical models for probe-level, oligonucleotide array data based on a multiplicative parametrization. In earlier work, we proposed similar analysis-of-variance-style mixed models fit on a log scale. With only subtle differences in the specification of their mean and stochastic error components, a question arises as to whether these models could lead to varying conclusions in practical application.
Results: In this paper, we provide an empirical comparison of the two models using a real data set, and find the models perform quite similarly across most genes, but with some interesting and important distinctions. We also present results from a simulation study designed to assess inferential properties of the models, and propose a modified test statistic for the Li–Wong model that provides an improvement in Type 1 error control. Advantages of both methods include the ability to directly assess and account for key sources of variability in the chip data and a means to automate statistical quality control.
Availability: The Li–Wong models are available in dChip: http://www.biostat.harvard.edu/complab/dchip/, and both methods will be commercially available in the forthcoming SAS Microarray Solution.
Supplementary information: Supplementary material is available at http://statgen.ncsu.edu/ggibson/Pubs.htm
Tailored gene array databases: applications in mechanistic toxicology
Authors: Karpinets Tatiana V. , Foy Brent D. , Frazier John M. 
Published: February 2004

Abstract
Motivation: The development of an annotated global database suitable for a wide range of investigations is a challenging and labor-intensive task. Thus, the development of databases tailored for specific applications remains necessary. For example, in the field of toxicology, no annotated gene array databases are now available that may assist in the correlation of changes in gene activity to cellular functions and processes associated with the toxic response.
Results: As an example of a tailored annotated database, an attempt was made to systematize available biological information on genes present on the Affymetrix Rat Toxicology U34 GeneChip, with a focus on how the gene products relate to liver cells and their response to chemical toxins. The information collected was imbedded in a local relational database to analyze data obtained in toxicological gene array experiments with hydrazine–exposed hepatocytes. The advantages and benefits of the tailored database in the biological interpretation of the results are demonstrated.
Availability: The database and gene expression data set are available at http://www.wright.edu/~tatiana.karpinets/
Reducing the variability in cDNA microarray image processing by Bayesian inference
Authors: Lawrence Neil D. , Milo Marta , Niranjan Mahesan , Rashbass Penny , Soullier Stephan 
Published: February 2004

Abstract
Motivation: Gene expression levels are obtained from microarray experiments through the extraction of pixel intensities from a scanned image of the slide. It is widely acknowledged that variabilities can occur in expression levels extracted from the same images by different users with the same software packages. These inconsistencies arise due to differences in the refinement of the placement of the microarray ‘grids’. We introduce a novel automated approach to the refinement of grid placements that is based upon the use of Bayesian inference for determining the size, shape and positioning of the microarray ‘spots’, capturing uncertainty that can be passed to downstream analysis.
Results: Our experiments demonstrate that variability between users can be significantly reduced using the approach. The automated nature of the approach also saves hours of researchers’ time normally spent in refining the grid placement.
Availability: A MATLAB implementation of the algorithm and tiff images of the slides used in our experiments, as well as the code necessary to recreate them are available for non-commercial use from http://www.dcs.shef.ac.uk/~neil/VIS
Fundamentals of massive automatic pairwise alignments of protein sequences: theoretical significance of Z-value statistics
Authors: Bastien Olivier , Aude Jean-Christophe , Roy Sylvaine , Maréchal Eric 
Published: February 2004

Abstract
Motivation:Different automatic methods of sequence alignments are routinely used as a starting point for homology searches and function inference. Confidence in an alignment probability is one of the major fundamentals of massive automatic genome-scale pairwise comparisons, for clustering of putative orthologs and paralogs, sequenced genome annotation or multiple-genomic tree constructions. Extreme value distribution based on the Karlin–Altschul model, usually advised for large-scale comparisons are not always valid, particularly in the case of comparisons of non-biased with nucleotide-biased genomes (such that of Plasmodium falciparum). Z-values estimates based on Monte Carlo technics, can be calculated experimentally for any alignment output, whatever the method used. Empirically, a Z-value higher than ∼8 is supposed reasonable to assess that an alignment score is significant, but this arbitrary figure was never theoretically justified.
Results: In this paper, we used the Bienaymé–Chebyshev inequality to demonstrate a theorem of the upper limit of an alignment score probability (or P-value). This theorem implies that a computed Z-value is a statistical test, a single-linkage clustering criterion and that 1/Z-value2 is an upper limit to the probability of an alignment score whatever the actual probability law is. Therefore, this study provides the missing theoretical link between a Z-value cut-off used for an automatic clustering of putative orthologs and/or paralogs, and the corresponding statistical risk in such genome-scale comparisons (using non-biased or biased genomes).
ChromoWheel: a new spin on eukaryotic chromosome visualization
Authors: Ekdahl Sven , Sonnhammer Erik L. L. 
Published: February 2004

Abstract
Summary: ChromoWheel is an Internet browser application for generating whole-genome illustrations. It can be used to depict chromosomes, genes and relations between chromosomal loci. The circular layout of chromosomes is advantageous for showing relationships between different chromosomes, as the connecting line never crosses over a chromosome. All graphical image components are in the vector-based format Scalable Vector Graphics, which are highly scaleable and admit user interaction. ChromoWheel can either be run with user-provided data in the generic SFS format, or as a browser front-end for precompiled genomic data.
Availability: http://chromowheel.cgb.ki.se
Automated extraction of mutation data from the literature: application of MuteXt to G protein-coupled receptors and nuclear hormone receptors
Authors: Horn Florence , Lau Anthony L. , Cohen Fred E. 
Published: February 2004

Abstract
Motivation: The amount of genomic and proteomic data that is published daily in the scientific literature is outstripping the ability of experimental scientists to stay current. Reviews, the traditional medium for collating published observations, are also unable to keep pace. For some specific classes of information (e.g. sequences and protein structures), obligatory data deposition policies have helped. However, a great deal of other valuable information is spread throughout the literature hindering coherent access. We are involved in the Molecular Class-Specific Information System (MCSIS) project, a collaborative effort to design and automate the maintenance of protein family databases. The first two databases, the GPCRDB and NucleaRDB, are focused on G protein-coupled receptors (GPCRs) and nuclear hormone receptors (NRs), respectively. The main aim of the MCSIS project is to gather heterogeneous data from across a variety of electronic and literature sources in order to draw new inferences about the target protein families.
Results: We present a computational method that identifies and extracts mutation data from the scientific literature. We focused on the extraction of single point mutations for the GPCR and NR superfamilies. After validation by plausibility filters, the mutation data is integrated into the corresponding MCSIS where it is combined with structural and sequence information already stored in these databases. We extracted and validated 2736 true point mutations from 914 articles on GPCRs and 785 true point mutations from 1094 articles on NRs. The current version of our automated extraction algorithm identifies 49.3% of the GPCR point mutations with a specificity of 87.9%, and 64.5% of the NR point mutations with a specificity of 85.8%. MuteXt routinely analyzes 100 electronic articles in approximately 1 h.
Availability: Extracted results are available via the GPCRDB and NucleaRDB at http://www.gpcr.org/7tm/mutation/ and http://www.receptors.org/NR/mutation/, respectively. The algorithm is available upon request.
Extracting human protein interactions from MEDLINE using a full-sentence parser
Authors: Daraselia Nikolai , Yuryev Anton , Egorov Sergei , Novichkova Svetalana , Nikitin Alexander , Mazo Ilya 
Published: March 2004

Abstract
Motivation: The living cell is a complex machine that depends on the proper functioning of its numerous parts, including proteins. Understanding protein functions and how they modify and regulate each other is the next great challenge for life-sciences researchers. The collective knowledge about protein functions and pathways is scattered throughout numerous publications in scientific journals. Bringing the relevant information together becomes a bottleneck in a research and discovery process. The volume of such information grows exponentially, which renders manual curation impractical. As a viable alternative, automated literature processing tools could be employed to extract and organize biological data into a knowledge base, making it amenable to computational analysis and data mining.
Results: We present MedScan, a completely automated natural language processing-based information extraction system. We have used MedScan to extract 2976 interactions between human proteins from MEDLINE abstracts dated after 1988. The precision of the extracted information was found to be 91%. Comparison with the existing protein interaction databases BIND and DIP revealed that 96% of extracted information is novel. The recall rate of MedScan was found to be 21%. Additional experiments with MedScan suggest that MEDLINE is a unique source of diverse protein function information, which can be extracted in a completely automated way with a reasonably high precision. Further directions of the MedScan technology improvement are discussed.
Availability: MedScan is available for commercial licensing from Ariadne Genomics, Inc.
FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes
Authors: Al-Shahrour Fátima , Díaz-Uriarte Ramón , Dopazo Joaquín 
Published: February 2004

Abstract
Summary: We present a simple but powerful procedure to extract Gene Ontology (GO) terms that are significantly over- or under-represented in sets of genes within the context of a genome-scale experiment (DNA microarray, proteomics, etc.). Said procedure has been implemented as a web application, FatiGO, allowing for easy and interactive querying. FatiGO, which takes the multiple-testing nature of statistical contrast into account, currently includes GO associations for diverse organisms (human, mouse, fly, worm and yeast) and the TrEMBL/Swissprot GOAnnotations@EBI correspondences from the European Bioinformatics Institute.
Availability: http://fatigo.bioinfo.cnio.es
CHOISS for selection of single nucleotide polymorphism markers on interval regularity
Authors: Lee Soohyun , Kang Changwon 
Published: February 2004

Abstract
Summary: We developed algorithms that find a set of single nucleotide polymorphism (SNP) markers based on interval regularity, given either the number of SNPs to choose (m) or the desired interval (I), subject to minimum variance or minimum sum of squared deviations from I. In both cases, the number of all possible sets increases exponentially with respect to the number of input SNPs (n), but our algorithms find the minima only with O(n2) calculations and comparisons by elimination of redundancy.
Availability: A Windows executable program CHOISS is freely available at http://biochem.kaist.ac.kr/choiss.htm
Supplementary information: http://biochem.kaist.ac.kr/choiss.htm
AntiHunter: searching BLAST output for EST antisense transcripts
Authors: Lavorgna Giovanni , Sessa Luca , Guffanti Alessandro , Lassandro Lelio , Casari Giorgio 
Published: February 2004

Abstract
Summary: AntiHunter is a new web-based tool for the identification of expressed sequence tag (EST) antisense transcripts from BLAST output. In order to perform an analysis, user is required to input a genomic sequence plus an associated list of transcript names and coordinates of the genomic region (i.e. genome annotation). After masking the repeated regions (if any), program will perform a BLASTN search of the input sequence versus the selected EST database, reporting by Email the EST entries that reveal a putative antisense transcript with respect to the user supplied list.
Availability: AntiHunter is currently available through a web interface at http://bio.ifom-firc.it/ANTIHUNTER/.
me-PCR: a refined ultrafast algorithm for identifying sequence-defined genomic elements
Authors: Murphy Kevin , Raj Towfique , Winters R. Scott , White Peter S. 
Published: February 2004

Abstract
Summary: We have adapted the originally described electronic PCR (e-PCR) algorithm to perform string searches more accurately and much more rapidly than previously possible. Our implementation [multithreaded e-PCR (me-PCR)] runs sufficiently fast to allow even desktop machines to query quickly large genomes with very large genomic element sets. In addition, me-PCR is multithreaded, interprets all IUPAC nucleotide symbols, allows searches with elements specified by long sequences (such as SNPs), accepts ranges in the expected PCR size input field, requires substantially less memory for analysis of large sequences and corrects a number of minor flaws causing misreporting of hits in exceptional cases. Thus, me-PCR provides increased annotation capabilities for complex genomes to non-expert laboratories.
Availability: me-PCR has been compiled for Linux, Solaris, AIX, Windows and Macintosh platforms. Both source and executable code are freely available to the research community at http://genome.chop.edu/mePCR/
The PCR Suite
Authors: van Baren Marijke J. , Heutink Peter 
Published: February 2004

Abstract
Summary: The web application PCR Suite is an extension of the primer design program Primer3. It allows the design of primer sets encompassing single nucleotide polymorphisms, all exons of a single gene, all open reading frames in a list of cDNAs or the creation of overlapping PCR products.
Availability: www.eur.nl/fgg/kgen/primer