By Topic

A Weighted Power Framework for Integrating Multisource Information: Gene Function Prediction in Yeast

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Shubhra Sankar Ray ; Center for Soft Computing Research: A National Facility, Indian Statistical Institute, Kolkata, India ; Sanghamitra Bandyopadhyay ; Sankar K. Pal

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, where one can download WPBS, related files, and a MATLAB code to predict functions of unclassified genes.

Published in:

IEEE Transactions on Biomedical Engineering  (Volume:59 ,  Issue: 4 )