bluuebunny/crossref_metadata_2025_split huggingface dataset:
On commuting operator exponentials
Authors: Paliogiannis Fotios 
Published: September 2003

Let

A
A

,

B
B

be bounded operators on a Banach space with

2
π
i

2\pi i

-congruence-free spectra such that

e
A

e
B

=

e
B

e
A

e^Ae^B=e^Be^A

. E. M. E. Wermuth has shown that

A
B
=
B
A

AB=BA

. Ch. Schmoeger later established this result, using inner derivations and, in a second paper, has shown that: for

a
,
b

a,b

in a complex unital Banach algebra, if the spectrum of

a
+
b

a+b

is

2
π
i

2\pi i

-congruence-free and

e
a

e
b

=

e

a
+
b

=

e
b

e
a

e^ae^b=e^{a+b}=e^be^a

, then

a
b
=
b
a

ab=ba

(and thus, answering an open problem raised by E. M. E. Wermuth). In this paper we use the holomorphic functional calculus to give alternative simple proofs of both of these results. Moreover, we use the Borel functional calculus to give new proofs of recent results of Ch. Schmoeger concerning normal operator exponentials on a complex Hilbert space, under a weaker hypothesis on the spectra.
PRECIS: Protein reports engineered from concise information in SWISS-PROT
Authors: Mitchell A.L. , Reich J.R. , Attwood T.K. 
Published: September 2003

Abstract
Motivation: There have been several endeavours to address the problem of annotating sequence data computationally, but the task is non-trivial and few tools have emerged that gather useful information on a given sequence, or set of sequences, in a simple and convenient manner. As more genome projects bear fruit, the mass of uncharacterized sequence data accumulating in public repositories grows ever larger. There is thus a pressing need for tools to support the process of automatic analysis and annotation of newly determined sequences. With this in mind, we have developed PRECIS, which automatically creates protein reports from sets of SWISS-PROT entries, collating results into structured reports, detailing known biological and medical information, literature and database cross-references, and relevant keywords.
Availability: The software is accessible online at: http://www.bioinf.man.ac.uk/cgi-bin/dbbrowser/precis/blast_precis.cgi
ESTAP—an automated system for the analysis of EST data
Authors: Mao Chunhong , Cushman John C. , May Gregory D. , Weller Jennifer W. 
Published: September 2003

Abstract
Summary: The EST Analysis Pipeline (ESTAP) is a set of analytical procedures that automatically verify, cleanse, store and analyze ESTs generated on high-throughput platforms. It uses a relational database to store sequence data and analysis results, which facilitates both the search for specific information and statistical analysis. ESTAP provides for easy viewing of the original and cleansed data, as well as the analysis results via a Web browser. It also allows the data owner to submit selected sequences to dbEST in a semi-automated fashion.
Availablity: http://www.vbi.vt.edu/~estap
Statistical significance analysis of longitudinal gene expression data
Authors: Guo Xu , Qi Huilin , Verfaillie Catherine M. , Pan Wei 
Published: September 2003

Abstract
Motivation: Time-course microarray experiments are designed to study biological processes in a temporal fashion. Longitudinal gene expression data arise when biological samples taken from the same subject at different time points are used to measure the gene expression levels. It has been observed that the gene expression patterns of samples of a given tumor measured at different time points are likely to be much more similar to each other than are the expression patterns of tumor samples of the same type taken from different subjects. In statistics, this phenomenon is called the within-subject correlation of repeated measurements on the same subject, and the resulting data are called longitudinal data. It is well known in other applications that valid statistical analyses have to appropriately take account of the possible within-subject correlation in longitudinal data.
Results: We apply estimating equation techniques to construct a robust statistic, which is a variant of the robust Wald statistic and accounts for the potential within-subject correlation of longitudinal gene expression data, to detect genes with temporal changes in expression. We associate significance levels to the proposed statistic by either incorporating the idea of the significance analysis of microarrays method or using the mixture model method to identify significant genes. The utility of the statistic is demonstrated by applying it to an important study of osteoblast lineage-specific differentiation. Using simulated data, we also show pitfalls in drawing statistical inference when the within-subject correlation in longitudinal gene expression data is ignored.
MedScan, a natural language processing engine for MEDLINE abstracts
Authors: Novichkova Svetlana , Egorov Sergei , Daraselia Nikolai 
Published: September 2003

Abstract
Motivation: The importance of extracting biomedical information from scientific publications is well recognized. A number of information extraction systems for the biomedical domain have been reported, but none of them have become widely used in practical applications. Most proposals to date make rather simplistic assumptions about the syntactic aspect of natural language. There is an urgent need for a system that has broad coverage and performs well in real-text applications.
Results: We present a general biomedical domain-oriented NLP engine called MedScan that efficiently processes sentences from MEDLINE abstracts and produces a set of regularized logical structures representing the meaning of each sentence. The engine utilizes a specially developed context-free grammar and lexicon. Preliminary evaluation of the system's performance, accuracy, and coverage exhibited encouraging results. Further approaches for increasing the coverage and reducing parsing ambiguity of the engine, as well as its application for information extraction are discussed.
Availability: MedScan is available for commercial licensing from Ariadne Genomics, Inc.
SignalViewer: analyzing microarray images
Authors: Laws R.J. , Bergemann T.L. , Quiaoit F. , Zhao L.P. 
Published: September 2003

Abstract
Summary: Microarray technology is now routinely used to monitor genome-wide expression profiles. However, current microarray imaging and analysis packages typically require manual intervention and assumptions on alignments. Unfortunately, limitations and assumptions are typically undisclosed and methods are not published. To facilitate exploration of image data, we developed SignalViewer. This paper presents a description of the application.
Availability: SignalViewer is available at http://qge.fhcrc.org/signalviewer/
Supplementary information: Screenshots are available at the above location, along with downloads for Windows 2000 and Linux (Redhat 7.2).
Diametrical clustering for identifying anti-correlated gene clusters
Authors: Dhillon Inderjit S. , Marcotte Edward M. , Roshan Usman 
Published: September 2003

Abstract
Motivation: Clustering genes based upon their expression patterns allows us to predict gene function. Most existing clustering algorithms cluster genes together when their expression patterns show high positive correlation. However, it has been observed that genes whose expression patterns are strongly anti-correlated can also be functionally similar. Biologically, this is not unintuitive—genes responding to the same stimuli, regardless of the nature of the response, are more likely to operate in the same pathways.
Results: We present a new diametrical clustering algorithm that explicitly identifies anti-correlated clusters of genes. Our algorithm proceeds by iteratively (i) re-partitioning the genes and (ii) computing the dominant singular vector of each gene cluster; each singular vector serving as the prototype of a 'diametric' cluster. We empirically show the effectiveness of the algorithm in identifying diametrical or anti-correlated clusters. Testing the algorithm on yeast cell cycle data, fibroblast gene expression data, and DNA microarray data from yeast mutants reveals that opposed cellular pathways can be discovered with this method. We present systems whose mRNA expression patterns, and likely their functions, oppose the yeast ribosome and proteosome, along with evidence for the inverse transcriptional regulation of a number of cellular systems.
Availability: See http://bioinformatics.icmb.utexas.edu for the experimental results. Software is available on request.
Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data
Authors: Wu Baolin , Abbott Tom , Fishman David , McMurray Walter , Mor Gil , Stone Kathryn , Ward David , Williams Kenneth , Zhao Hongyu 
Published: September 2003

Abstract
Motivation: Novel methods, both molecular and statistical, are urgently needed to take advantage of recent advances in biotechnology and the human genome project for disease diagnosis and prognosis. Mass spectrometry (MS) holds great promise for biomarker identification and genome-wide protein profiling. It has been demonstrated in the literature that biomarkers can be identified to distinguish normal individuals from cancer patients using MS data. Such progress is especially exciting for the detection of early-stage ovarian cancer patients. Although various statistical methods have been utilized to identify biomarkers from MS data, there has been no systematic comparison among these approaches in their relative ability to analyze MS data.
Results: We compare the performance of several classes of statistical methods for the classification of cancer based on MS spectra. These methods include: linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbor classifier, bagging and boosting classification trees, support vector machine, and random forest (RF). The methods are applied to ovarian cancer and control serum samples from the National Ovarian Cancer Early Detection Program clinic at Northwestern University Hospital. We found that RF outperforms other methods in the analysis of MS data.
Supplementary information: http://bioinformatics.med.yale.edu/proteomics/BioSupp1.html
Consensus alignment for reliable framework prediction in homology modeling
Authors: Prasad J.C. , Comeau S.R. , Vajda S. , Camacho C.J. 
Published: September 2003

Abstract
Motivation: Even the best sequence alignment methods frequently fail to correctly identify the framework regions for which backbones can be copied from the template into the target structure. Since the underprediction and, more significantly, the overprediction of these regions reduces the quality of the final model, it is of prime importance to attain as much as possible of the true structural alignment between target and template.
Results: We have developed an algorithm called Consensus that consistently provides a high quality alignment for comparative modeling. The method follows from a benchmark analysis of the 3D models generated by ten alignment techniques for a set of 79 homologous protein structure pairs. For 20-to-40% of the targets, these methods yield models with at least 6 Å root mean square deviation (RMSD) from the native structure. We have selected the top five performing methods, and developed a consensus algorithm to generate an improved alignment. By building on the individual strength of each method, a set of criteria was implemented to remove the alignment segments that are likely to correspond to structurally dissimilar regions. The automated algorithm was validated on a different set of 48 protein pairs, resulting in 2.2 Å average RMSD for the predicted models, and only four cases in which the RMSD exceeded 3 Å. The average length of the alignments was about 75% of that found by standard structural superposition methods. The performance of Consensus was consistent from 2 to 32% target–template sequence identity, and hence it can be used for accurate prediction of framework regions in homology modeling.
Availability: The algorithm is available as a server at http://structure.bu.edu/cgi-bin/consensus/consensus.cgi
Comparison of sequence masking algorithms and the detection of biased protein sequence regions
Authors: Kreil David P. , Ouzounis Christos A. 
Published: September 2003

Abstract
Motivation: Separation of protein sequence regions according to their local information complexity and subsequent masking of low complexity regions has greatly enhanced the reliability of function prediction by sequence similarity. Comparisons with alternative methods that focus on compositional sequence bias rather than information complexity measures have shown that removal of compositional bias yields at least as sensitive and much more specific results. Besides the application of sequence masking algorithms to sequence similarity searches, the study of the masked regions themselves is of great interest. Traditionally, however, these have been neglected despite evidence of their functional relevance.
Results: Here we demonstrate that compositional bias seems to be a more effective measure for the detection of biologically meaningful signals. Typical results on proteins are compared to results for sequences that have been randomized in various ways, conserving composition and local correlations for individual proteins or the entire set. It is remarkable that low-complexity regions have the same form of distribution in proteins as in randomized sequences, and that the signal from randomized sequences with conserved local correlations and amino acid composition almost matches the signal from proteins. This is not the case for sequence bias, which hence seems to be a genuinely biological phenomenon in contrast to patches of low complexity.
Availability: Software in executable form is available on request from the authors.
Supplementary information: There is an online supplement with additional supporting figures. (http://www.inference.phy.cam.ac.uk/dpk20/sup/)
Detecting putative orthologs
Authors: Wall D. P. , Fraser H. B. , Hirsh A. E. 
Published: September 2003

Abstract
Summary: We developed an algorithm that improves upon the common procedure of taking reciprocal best blast hits (rbh) in the identification of orthologs. The method—reciprocal smallest distance algorithm (rsd)—relies on global sequence alignment and maximum likelihood estimation of evolutionary distances to detect orthologs between two genomes. rsd finds many putative orthologs missed by rbh because it is less likely than rbh to be misled by the presence of a close paralog.
Availability: A Python program and ReadMe file are freely available from: http://charles.stanford.edu/~dennis/research.html
GFPE: gene-finding program evaluation
Authors: Wang Jian , Kraemer Eileen 
Published: September 2003

Abstract
Summary: Gene-finding program evaluation (GFPE) is a set of Java classes for evaluating gene-finding programs. A command-line interface is also provided. Inputs to the program include the sequence data (in FASTA format), annotations of 'actual' sequence features, and annotations of 'predicted' sequence features. Annotation files are in the General Feature Format promoted by the Sanger center. GFPE calculates a number of metrics of accuracy of predictions at three levels: the coding level, the exon level, and the protein level.
Availability: The program is freely available at: ftp://anonymous@iubio.bio.indiana.edu/molbio/genefind/
PathMiner: predicting metabolic pathways by heuristic search
Authors: McShan D.C. , Rao S. , Shah I. 
Published: September 2003

Abstract
Motivation: Automated methods for biochemical pathway inference are becoming increasingly important for understanding biological processes in living and synthetic systems. With the availability of data on complete genomes and increasing information about enzyme-catalyzed biochemistry it is becoming feasible to approach this problem computationally. In this paper we present PathMiner, a system for automatic metabolic pathway inference. PathMiner predicts metabolic routes by reasoning over transformations using chemical and biological information.
Results: We build a biochemical state-space using data from known enzyme-catalyzed transformations in Ligand, including, 2917 unique transformations between 3890 different compounds. To predict metabolic pathways we explore this state-space by developing an informed search algorithm. For this purpose we develop a chemically motivated heuristic to guide the search. Since the algorithm does not depend on predefined pathways, it can efficiently identify plausible routes using known biochemical transformations.
Availability: The system is available for testing at http://pathminer.uchsc.edu
FindGDPs: identification of primers for labeling microbial transcriptomes for DNA microarray analysis
Authors: Blick Robert J. , Revel Andrew T. , Hansen Eric J. 
Published: September 2003

Abstract
Summary: FindGDPs is a program that uses a greedy algorithm to quickly identify a set of genome-directed primers that specifically anneal to all of the open reading frames in a genome and that do not exhibit full-length complementarity to the members of another user-supplied set of nucleotide sequences.
Availability: The program code is distributed under the GNU General Public License at http://www3.utsouthwestern.edu/microbiology/pages/faculty/hansen.html
A comparative analysis of HGSC and Celera human genome assemblies and gene sets
Authors: Li Shuyu , Cutler Gene , Liu Jane Jijun , Hoey Timothy , Chen Liangbiao , Schultz Peter G. , Liao Jiayu , Ling Xuefeng Bruce 
Published: September 2003

Abstract
Motivation: Since the simultaneous publication of the human genome assembly by the International Human Genome Sequencing Consortium (HGSC) and Celera Genomics, several comparisons have been made of various aspects of these two assemblies. In this work, we set out to provide a more comprehensive comparative analysis of the two assemblies and their associated gene sets.
Results: The local sequence content for both draft genome assemblies has been similar since the early releases, however it took a year for the quality of the Celera assembly to approach that of HGSC, suggesting an advantage of HGSC's hierarchical shotgun (HS) sequencing strategy over Celera's whole genome shotgun (WGS) approach. While similar numbers of ab initio predicted genes can be derived from both assemblies, Celera's Otto approach consistently generated larger, more varied gene sets than the Ensembl gene build system. The presence of a non-overlapping gene set has persisted with successive data releases from both groups. Since most of the unique genes from either genome assembly could be mapped back to the other assembly, we conclude that the gene set discrepancies do not reflect differences in local sequence content but rather in the assemblies and especially the different gene-prediction methodologies.
ProSAT: functional annotation of protein 3D structures
Authors: Gabdoulline Razif R. , Hoffmann René , Leitner Florian , Wade Rebecca C. 
Published: September 2003

Abstract
Summary: ProSAT (for Protein Structure Annotation Tool) is a tool to facilitate interactive visualization of non-structure-based functional annotations in protein 3D structures. It performs automated mapping of the functional annotations onto the protein structure and allows functional sites to be readily identified upon visualization. The current version of ProSAT can be applied to large datasets of protein structures for fast visual identification of active and other functional sites derived from the SwissProt and Prosite databases.
Availability: http://projects.villa-bosch.de/mcm/database/prosat/
E-Cell 2: Multi-platform E-Cell simulation system
Authors: Takahashi K. , Ishikawa N. , Sadamoto Y. , Sasamoto H. , Ohta S. , Shiozawa A. , Miyoshi F. , Naito Y. , Nakayama Y. , Tomita M. 
Published: September 2003

Abstract
Summary: A new version of the E-Cell simulation system, which runs on Windows as well as Linux, has been released as free software under the terms of the GNU General Public License.
Availability: Downloadable from http://www.E-Cell.org
Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs
Authors: Park Keun-Joon , Kanehisa Minoru 
Published: September 2003

Abstract
Motivation: The subcellular location of a protein is closely correlated to its function. Thus, computational prediction of subcellular locations from the amino acid sequence information would help annotation and functional prediction of protein coding genes in complete genomes. We have developed a method based on support vector machines (SVMs).
Results: We considered 12 subcellular locations in eukaryotic cells: chloroplast, cytoplasm, cytoskeleton, endoplasmic reticulum, extracellular medium, Golgi apparatus, lysosome, mitochondrion, nucleus, peroxisome, plasma membrane, and vacuole. We constructed a data set of proteins with known locations from the SWISS-PROT database. A set of SVMs was trained to predict the subcellular location of a given protein based on its amino acid, amino acid pair, and gapped amino acid pair compositions. The predictors based on these different compositions were then combined using a voting scheme. Results obtained through 5-fold cross-validation tests showed an improvement in prediction accuracy over the algorithm based on the amino acid composition only. This prediction method is available via the Internet.
Availability: http://www.genome.ad.jp/SIT/ploc.html
Supplementary information: http://web.kuicr.kyoto-u.ac.jp/~park/Seqdata/
Secondary structure prediction with support vector machines
Authors: Ward J. J. , McGuffin L. J. , Buxton B. F. , Jones D. T. 
Published: September 2003

Abstract
Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem.
Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure.
Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods.
Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.
CRANN: detecting adaptive evolution in protein-coding DNA sequences
Authors: Creevey C.J. , McInerney J.O. 
Published: September 2003

Abstract
Summary: A software program CRANN has been developed in order to detect adaptive evolution in protein-coding DNA sequences.
Availability: CRANN is available from http://bioinf.may.ie/crann/
Supplementary Information: CRANN has been written in the C programming language. Source code is available on request.