A weighted power framework for integrating multisource information: gene function prediction in yeast.
Predicting the functions of unannotated genes is one of the major challenges of biological investigation. In this study, we propose a weighted power scoring framework, called weighted power biological score (WPBS), for combining different biological data sources and predicting the function of some of the unclassified yeast Saccharomyces cerevisiae genes. The relative power and weight coefficients of different data sources, in the proposed score, are estimated systematically by utilizing functional annotations [yeast Gene Ontology (GO)-Slim: Process] of classified genes, available from Saccharomyces Genome Database. Genes are then clustered by applying k-medoids algorithm on WPBS, and functional categories of 334 unclassified genes are predicted using a P-value cutoff 1 ×10(-5). The WPBS is available online at http://www.isical.ac.in/~ shubhra/WPBS/WPBS.html, where one can download WPBS, related files, and a MATLAB code to predict functions of unclassified genes.
Center for Soft Computing Research: A National Facility, Indian Statistical Institute, Kolkata, India. shubhra@isical.ac.in
Shubhra Sankar
SS
Ray
Sanghamitra
S
Bandyopadhyay
Sankar K
SK
Pal
D000595:Amino Acid Sequence; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D008969:Molecular Sequence Data; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D013329:Structure-Activity Relationship; D018511:Systems Integration
D029701:Saccharomyces cerevisiae Proteins