DocumentCode :
1439714
Title :
A Weighted Power Framework for Integrating Multisource Information: Gene Function Prediction in Yeast
Author :
Ray, Shubhra Sankar ; Bandyopadhyay, Sanghamitra ; Pal, Sankar K.
Author_Institution :
Center for Soft Comput. Res.: A Nat. Facility, Indian Stat. Inst., Kolkata, India
Volume :
59
Issue :
4
fYear :
2012
fDate :
4/1/2012 12:00:00 AM
Firstpage :
1162
Lastpage :
1168
Abstract :
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.
Keywords :
biological techniques; biology computing; genetics; molecular biophysics; Saccharomyces Genome Database; biological data source; gene function prediction; k-medoids algorithm; multisource information; weighted power biological score; weighted power scoring framework; yeast Saccharomyces cerevisiae gene; Biological information theory; Correlation; Databases; Gene expression; Protein engineering; Proteins; Combinatorial optimization; gene expression; phenotypic profile; protein sequence; transitive homology; Amino Acid Sequence; Computer Simulation; Data Mining; Databases, Protein; Gene Expression Profiling; Models, Biological; Molecular Sequence Data; Protein Interaction Mapping; Saccharomyces cerevisiae; Saccharomyces cerevisiae Proteins; Signal Transduction; Structure-Activity Relationship; Systems Integration;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2012.2186689
Filename :
6145620
Link To Document :
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