Title: An integrated probabilistic approach for gene function prediction using multiple sources of high-throughput data

Authors: Chao Zhang, Trupti Joshi, Guan Ning Lin, Dong Xu

Addresses: Digital Biology Laboratory, Computer Science Department and Christopher S. Bond Life Sciences Centre, University of Missouri-Columbia, 1201 East Rollins Road, Columbia, MO 65211-2060, USA. ' Digital Biology Laboratory, Computer Science Department and Christopher S. Bond Life Sciences Centre, University of Missouri-Columbia, 1201 East Rollins Road, Columbia, MO 65211-2060, USA. ' Digital Biology Laboratory, Computer Science Department and Christopher S. Bond Life Sciences Centre, University of Missouri-Columbia, 1201 East Rollins Road, Columbia, MO 65211-2060, USA. ' Digital Biology Laboratory, Computer Science Department and Christopher S. Bond Life Sciences Centre, University of Missouri-Columbia, 1201 East Rollins Road, Columbia, MO 65211-2060, USA

Abstract: Characterising gene function is one of the major challenging tasks in the post-genomic era. Various approaches have been developed to integrate multiple sources of high-throughput data to predict gene function. Most of those approaches are just used for research purpose and have not been implemented as publicly available tools. Even for those implemented applications, almost all of them are still web-based |prediction servers| that have to be managed by specialists. This paper introduces a systematic method for integrating various sources of high-throughput data to predict gene function and analyse our prediction results and evaluates its performances based on the competition for mouse gene function prediction (MouseFunc). A stand-alone Java-based software package |GeneFAS| is freely available at http://digbio.missouri.edu/genefas.

Keywords: gene function prediction; high-throughput data; data integration; gene ontology; mouse gene functions; bioinformatics; computational biology.

DOI: 10.1504/IJCBDD.2008.021418

International Journal of Computational Biology and Drug Design, 2008 Vol.1 No.3, pp.254 - 274

Available online: 26 Nov 2008 *

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