bluuebunny/crossref_metadata_2025_split huggingface dataset:
A benchmark for Affymetrix GeneChip expression measures
Authors: Cope Leslie M. , Irizarry Rafael A. , Jaffee Harris A. , Wu Zhijin , Speed Terence P. 
Published: February 2004

Abstract
Motivation: The defining feature of oligonucleotide expression arrays is the use of several probes to assay each targeted transcript. This is a bonanza for the statistical geneticist, who can create probeset summaries with specific characteristics. There are now several methods available for summarizing probe level data from the popular Affymetrix GeneChips, but it is difficult to identify the best method for a given inquiry.
Results: We have developed a graphical tool to evaluate summaries of Affymetrix probe level data. Plots and summary statistics offer a picture of how an expression measure performs in several important areas. This picture facilitates the comparison of competing expression measures and the selection of methods suitable for a specific investigation. The key is a benchmark data set consisting of a dilution study and a spike-in study. Because the truth is known for these data, we can identify statistical features of the data for which the expected outcome is known in advance. Those features highlighted in our suite of graphs are justified by questions of biological interest and motivated by the presence of appropriate data.
Availability: In conjunction with the release of a graphics toolbox as part of the Bioconductor project (http://www.bioconductor.org), a webtool is available at http://affycomp.biostat.jhsph.edu. Supplemental material is available at http://www.biostat.jhsph.edu/~ririzarr/papers/suppaffycomp.pdf
Model-based methods for identifying periodically expressed genes based on time course microarray gene expression data
Authors: Luan Y. , Li H. 
Published: February 2004

Abstract
Motivation: The expressions of many genes associated with certain periodic biological and cell cycle processes such as circadian rhythm regulation are known to be rhythmic. Identification of the genes whose time course expressions are synchronized to certain periodic biological process may help to elucidate the molecular basis of many diseases, and these gene products may in turn represent drug targets relevant to those diseases.
Results: We propose in this paper a statistical framework based on a shape-invariant model together with a false discovery rate (FDR) procedure for identifying periodically expressed genes based on microarray time-course gene expression data and a set of known periodically expressed guide genes. We applied the proposed methods to the α-factor, cdc15 and cdc28 synchronized yeast cell cycle data sets and identified a total of 1010 cell-cycle-regulated genes at a FDR of 0.5% in at least one of the three data sets analyzed, including 89 (86%) of 104 known periodic transcripts. We also identified 344 and 201 circadian rhythmic genes in vivo in mouse heart and liver tissues with FDR of 10 and 2.5%, respectively. Our results also indicate that the shape-invariant model fits the data well and provides estimate of the common shape function and the relative phases for these periodically regulated genes.
Availability: Matlab programs are available on request from the authors.
Supplementary information: http://dna.ucdavis.edu/~hli/period.html
Functional topology in a network of protein interactions
Authors: Pržulj N. , Wigle D.A. , Jurisica I. 
Published: February 2004

Abstract
Motivation: The building blocks of biological networks are individual protein–protein interactions (PPIs). The cumulative PPI data set in Saccharomyces cerevisiae now exceeds 78 000. Studying the network of these interactions will provide valuable insight into the inner workings of cells.
Results: We performed a systematic graph theory-based analysis of this PPI network to construct computational models for describing and predicting the properties of lethal mutations and proteins participating in genetic interactions, functional groups, protein complexes and signaling pathways. Our analysis suggests that lethal mutations are not only highly connected within the network, but they also satisfy an additional property: their removal causes a disruption in network structure. We also provide evidence for the existence of alternate paths that bypass viable proteins in PPI networks, while such paths do not exist for lethal mutations. In addition, we show that distinct functional classes of proteins have differing network properties. We also demonstrate a way to extract and iteratively predict protein complexes and signaling pathways. We evaluate the power of predictions by comparing them with a random model, and assess accuracy of predictions by analyzing their overlap with MIPS database.
Conclusions: Our models provide a means for understanding the complex wiring underlying cellular function, and enable us to predict essentiality, genetic interaction, function, protein complexes and cellular pathways. This analysis uncovers structure–function relationships observable in a large PPI network.
Supplementary information: We are placing the full predicted tables on the web page: http://www.cs.utoronto.ca/~juris/data/b03/SuppDataTables.zip
An ontology for collaborative construction and analysis of cellular pathways
Authors: Demir E. , Babur O. , Dogrusoz U. , Gursoy A. , Ayaz A. , Gulesir G. , Nisanci G. , Cetin-Atalay R. 
Published: February 2004

Abstract
Motivation: As the scientific curiosity in genome studies shifts toward identification of functions of the genomes in large scale, data produced about cellular processes at molecular level has been accumulating with an accelerating rate. In this regard, it is essential to be able to store, integrate, access and analyze this data effectively with the help of software tools. Clearly this requires a strong ontology that is intuitive, comprehensive and uncomplicated.
Results: We define an ontology for an intuitive, comprehensive and uncomplicated representation of cellular events. The ontology presented here enables integration of fragmented or incomplete pathway information via collaboration, and supports manipulation of the stored data. In addition, it facilitates concurrent modifications to the data while maintaining its validity and consistency. Furthermore, novel structures for representation of multiple levels of abstraction for pathways and homologies is provided. Lastly, our ontology supports efficient querying of large amounts of data.
We have also developed a software tool named pathway analysis tool for integration and knowledge acquisition (PATIKA) providing an integrated, multi-user environment for visualizing and manipulating network of cellular events. PATIKA implements the basics of our ontology.
Availability: PATIKA version 1.0 beta is available upon request at http://www.patika.org
Advanced significance analysis of microarray data based on weighted resampling: a comparative study and application to gene deletions in Mycobacterium bovis
Authors: Kutalik Zoltan , Inwald Jacqueline , Gordon Steve V. , Hewinson R. Glyn , Butcher Philip , Hinds Jason , Cho Kwang-Hyun , Wolkenhauer Olaf 
Published: February 2004

Abstract
Motivation: When analyzing microarray data, non-biological variation introduces uncertainty in the analysis and interpretation. In this paper we focus on the validation of significant differences in gene expression levels, or normalized channel intensity levels with respect to different experimental conditions and with replicated measurements. A myriad of methods have been proposed to study differences in gene expression levels and to assign significance values as a measure of confidence. In this paper we compare several methods, including SAM, regularized t-test, mixture modeling, Wilk's lambda score and variance stabilization. From this comparison we developed a weighted resampling approach and applied it to gene deletions in Mycobacterium bovis.
Results: We discuss the assumptions, model structure, computational complexity and applicability to microarray data. The results of our study justified the theoretical basis of the weighted resampling approach, which clearly outperforms the others.
Availability: Algorithms were implemented using the statistical programming language R and available on the author's web-page.
Supplementary information: For additional material see http://www.sbi.uni-rostock.de/
Non-parametric, hypothesis-based analysis of microarrays for comparison of several phenotypes
Authors: Kowalski Jeanne , Drake Charles , Schwartz Ronald H. , Powell Jonathan 
Published: February 2004

Abstract
Motivation: We present a statistical framework for the analysis of high-dimensional microarray data, where the goal is to compare intensities among several groups based on as few as a single sample from each group. In this setting, it is of interest to compare gene expression among several phenotypes to define candidate genes that simultaneously characterize several criteria, simultaneously, among the comparison groups. We motivate the approach by a comparative microarray experiment in which clones of a cell were singly exposed to several distinct but related conditions. The experiment was conducted to elucidate genes involved in pathways leading to T cell clonal anergy.
Results: By integrating inference principles within a bioinformatics setting, we introduce a two-stage approach to select candidate genes that characterize several criteria. The method is unified in its non-parametric approach to inference and description. For inference, we construct a testable hypothesis based on the criteria of interest in a high-dimensional space, while preserving the dependence among genes. Upon rejecting the null, we estimate the cardinality of a set of individual candidate genes (or gene pairs) that depict the events of interest. With this estimate, we then select individual genes (or gene pairs) based upon a two-dimensional ranking that examines relations within and between genes, among comparison groups, using singular value decomposition in combination with inner product concepts.
Availability: The functions developed for obtaining results from our approach are available upon request. A detailed documentation of the methods and the experiment may be obtained from http://www.cancerbiostats.onc.jhmi.edu/Kowalski.htm
Is cross-validation valid for small-sample microarray classification?
Authors: Braga-Neto Ulisses M. , Dougherty Edward R. 
Published: February 2004

Abstract
Motivation: Microarray classification typically possesses two striking attributes: (1) classifier design and error estimation are based on remarkably small samples and (2) cross-validation error estimation is employed in the majority of the papers. Thus, it is necessary to have a quantifiable understanding of the behavior of cross-validation in the context of very small samples.
Results: An extensive simulation study has been performed comparing cross-validation, resubstitution and bootstrap estimation for three popular classification rules—linear discriminant analysis, 3-nearest-neighbor and decision trees (CART)—using both synthetic and real breast-cancer patient data. Comparison is via the distribution of differences between the estimated and true errors. Various statistics for the deviation distribution have been computed: mean (for estimator bias), variance (for estimator precision), root-mean square error (for composition of bias and variance) and quartile ranges, including outlier behavior. In general, while cross-validation error estimation is much less biased than resubstitution, it displays excessive variance, which makes individual estimates unreliable for small samples. Bootstrap methods provide improved performance relative to variance, but at a high computational cost and often with increased bias (albeit, much less than with resubstitution).
Availability and Supplementary information: A companion web site can be accessed at the URL http://ee.tamu.edu/~edward/cv_paper. The companion web site contains: (1) the complete set of tables and plots regarding the simulation study; (2) additional figures; (3) a compilation of references for microarray classification studies and (4) the source code used, with full documentation and examples.
A graph-theoretic modeling on GO space for biological interpretation of gene clusters
Authors: Lee Sung Geun , Hur Jung Uk , Kim Yang Seok 
Published: February 2004

Abstract
Motivation: With the advent of DNA microarray technologies, the parallel quantification of genome-wide transcriptions has been a great opportunity to systematically understand the complicated biological phenomena. Amidst the enthusiastic investigations into the intricate gene expression data, clustering methods have been the useful tools to uncover the meaningful patterns hidden in those data. The mathematical techniques, however, entirely based on the numerical expression data, do not show biologically relevant information on the clustering results.
Results: We present a novel methodology for biological interpretation of gene clusters. Our graph theoretic algorithm extracts common biological attributes of the genes within a cluster or a group of interest through the modified structure of gene ontology (GO) called GO tree. After genes are annotated with GO terms, the hierarchical nature of GO terms is used to find the representative biological meanings of the gene clusters. In addition, the biological significance of gene clusters can be assessed quantitatively by defining a distance function on the GO tree. Our approach has a complementary meaning to many statistical clustering techniques; we can see clustering problems from a different viewpoint by use of biological ontology. We applied this algorithm to the well-known data set and successfully obtained the biological features of the gene clusters with the quantitative biological assessment of clustering quality through GO Biological Process.
Availability: The software is available on request from the authors.
Knowledge discovery by automated identification and ranking of implicit relationships
Authors: Wren Jonathan D. , Bekeredjian Raffi , Stewart Jelena A. , Shohet Ralph V. , Garner Harold R. 
Published: February 2004

Abstract
Motivation: New relationships are often implicit from existing information, but the amount and growth of published literature limits the scope of analysis an individual can accomplish. Our goal was to develop and test a computational method to identify relationships within scientific reports, such that large sets of relationships between unrelated items could be sought out and statistically ranked for their potential relevance as a set.
Results: We first construct a network of tentative relationships between ‘objects’ of biomedical research interest (e.g. genes, diseases, phenotypes, chemicals) by identifying their co-occurrences within all electronically available MEDLINE records. Relationships shared by two unrelated objects are then ranked against a random network model to estimate the statistical significance of any given grouping. When compared against known relationships, we find that this ranking correlates with both the probability and frequency of object co-occurrence, demonstrating the method is well suited to discover novel relationships based upon existing shared relationships. To test this, we identified compounds whose shared relationships predicted they might affect the development and/or progression of cardiac hypertrophy. When laboratory tests were performed in a rodent model, chlorpromazine was found to reduce the progression of cardiac hypertrophy.
Supplementary information: http://innovation.swmed.edu/IRIDESCENT/Supplemental_Info.htm
LDDist: a Perl module for calculating LogDet pair-wise distances for protein and nucleotide sequences
Authors: Thollesson Mikael 
Published: February 2004

Abstract
Summary: LDDist is a Perl module implemented in C++ that allows the user to calculate LogDet pair-wise genetic distances for amino acid as well as nucleotide sequence data. It can handle site-to-site rate variation by treating a proportion of the sites as invariant and/or by assigning sites to different, presumably homogenous, rate categories. The rate-class assignments and invariant proportion can be set explicitly, or estimated by the program; the latter using either of two different capture–recapture methods. The assignment to rate categories in lieu of a phylogeny can be done using Shannon–Wiener index as a crude token for relative rate.
Availability: LDDist and its companion Perl script PLD are freely available at http://artedi.ebc.uu.se/molev/software/LDDist.html
Predicting protein secondary structure by cascade-correlation neural networks
Authors: Wood Matthew J. , Hirst Jonathan D. 
Published: February 2004

Abstract
Summary: The back-propagation neural network algorithm is a commonly used method for predicting the secondary structure of proteins. Whilst popular, this method can be slow to learn and here we compare it with an alternative: the cascade-correlation architecture. Using a constructive algorithm, cascade-correlation achieves predictive accuracies comparable to those obtained by back-propagation, in shorter time.
Availability: A web server is available to a trained cascade-correlation neural network, with links to the source code for both algorithms.
SVM based method for predicting HLA-DRB1*0401 binding peptides in an antigen sequence
Authors: Bhasin Manoj , Raghava G. P. S. 
Published: February 2004

Abstract
Summary: Prediction of peptides binding with MHC class II allele HLA-DRB1*0401 can effectively reduce the number of experiments required for identifying helper T cell epitopes. This paper describes support vector machine (SVM) based method developed for identifying HLA-DRB1*0401 binding peptides in an antigenic sequence. SVM was trained and tested on large and clean data set consisting of 567 binders and equal number of non-binders. The accuracy of the method was 86% when evaluated through 5-fold cross-validation technique.
Available: A web server HLA-DR4Pred based on above approach is available at http://www.imtech.res.in/raghava/hladr4pred/ and http://bioinformatics.uams.edu/mirror/hladr4pred/ (Mirror Site).
Supplementary information: http://www.imtech.res.in/raghava/hladr4pred/info.html
Metrics for comparing regulatory sequences on the basis of pattern counts
Authors: van Helden Jacques 
Published: February 2004

Abstract
Motivation: Upstream sequences contain short motifs, which mediate transcriptional regulation by specifically binding different transcription factors. The presence of common motifs in the regulatory regions of two genes might be considered as a clue for a potential co-regulation. A pattern count-based (dis)similarity metric between sequences could thus be used to classify genes according to their putative regulatory properties.
Results: We present here several metrics which rely on probability theory, and which aim at comparing sequences on the basis of pattern counts. We compare these metrics to several classical dissimilarity and similarity metrics, and illustrate their behaviour with a biological example.
Supplementary information: The data, results, and R routines used in this paper are freely available at http://rsat.ulb.ac.be/rsat/published_data/pattern_count_metrics_2003/
PIVOT: Protein Interacions VisualizatiOn Tool
Authors: Orlev Nir , Shamir Ron , Shiloh Yosef 
Published: February 2004

Abstract
Summary: Protein Interaction VisualizatiOn Tool (PIVOT) is a visualization tool for protein–protein interactions. It allows the user to create personal data sets of interactions by combining information from private and public data sources. The user can gradually access the interactions' data using a clear interactive map that is focused on the researcher's protein of interest, and is reshaped and expanded in response to his/her queries. It also offers several visual enhancements and intelligent queries that help the user efficiently study it. PIVOT allows the user to search the interactions data set for paths connecting proteins that are expected to co-operate. The user can also employ PIVOT to predict unknown interactions among proteins, based on interactions among their homologous proteins in other species.
Availability: Freely available for academic users, at http://www.cs.tau.ac.il/~rshamir/pivot
Supplementary information: A demonstration video clip is available at http://www.cs.tau.ac.il/~rshamir/pivot/Demo.avi
Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference
Authors: Altekar Gautam , Dwarkadas Sandhya , Huelsenbeck John P. , Ronquist Fredrik 
Published: February 2004

Abstract
Motivation: Bayesian estimation of phylogeny is based on the posterior probability distribution of trees. Currently, the only numerical method that can effectively approximate posterior probabilities of trees is Markov chain Monte Carlo (MCMC). Standard implementations of MCMC can be prone to entrapment in local optima. Metropolis coupled MCMC [(MC)3], a variant of MCMC, allows multiple peaks in the landscape of trees to be more readily explored, but at the cost of increased execution time.
Results: This paper presents a parallel algorithm for (MC)3. The proposed parallel algorithm retains the ability to explore multiple peaks in the posterior distribution of trees while maintaining a fast execution time. The algorithm has been implemented using two popular parallel programming models: message passing and shared memory. Performance results indicate nearly linear speed improvement in both programming models for small and large data sets.
Availability: MrBayes v3.0 is available at http://morphbank.ebc.uu.se/mrbayes/
The Jalview Java alignment editor
Authors: Clamp Michele , Cuff James , Searle Stephen M. , Barton Geoffrey J. 
Published: February 2004

Abstract
Summary: Multiple sequence alignment remains a crucial method for understanding the function of groups of related nucleic acid and protein sequences. However, it is known that automatic multiple sequence alignments can often be improved by manual editing. Therefore, tools are needed to view and edit multiple sequence alignments. Due to growth in the sequence databases, multiple sequence alignments can often be large and difficult to view efficiently. The Jalview Java alignment editor is presented here, which enables fast viewing and editing of large multiple sequence alignments.
Availability: The Jar file and source code for Jalview is freely available via the World Wide Web at http://www.jalview.org. A Jalview mailing list is also available by e-mailing majordomo@sanger.ac.uk with subscribe Jalview in the body of the mail.
SEPON, a Selection and Evaluation Pipeline for OligoNucleotides based on ESTs with a non-target Tm algorithm for reducing cross-hybridization in microarray gene expression experiments
Authors: Hornshøj Henrik , Stengaard Henrik , Panitz Frank , Bendixen Christian 
Published: February 2004

Abstract
Summary: SEPON, Selection and Evaluation Pipeline for OligoNucleotide generates n-mer oligonucleotide sequences from expressed sequence tags of non-annotated genomes for microarray gene-expression profiling. A non-target melting temperature (Tm) algorithm will reduce cross-hybridization by estimating Tm of oligonucleotide hybridization to non-specific targets (non-target Tm) and discard oligonucleotides with non-target Tm estimate above user-defined threshold. SEPON allows user-defined filtering, predicts exon location, assigns penalty based on 3′ distance, GC content, secondary structure Tm and non-target Tm and ranks oligonucleotides for optimal selection.
Availability: The program is freely available for non-commercial and academic use under the GNU General Public License and is obtained upon request to authors.
Supplementary information: http://www.agrsci.dk/hag/sepon/