A weighted power framework for integrating multisource information: gene function prediction in yeast

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.