An integrated probabilistic approach for gene function prediction using multiple sources of high-throughput data.
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.eduigenefas.
Zhang
Chao
C
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. chaozhang@mizzou.edu
Joshi
Trupti
T
Lin
Guan Ning
GN
Xu
Dong
D
D000818:Animals; D001499:Bayes Theorem; D019295:Computational Biology; D003196:Computer Graphics; D003198:Computer Simulation; D030541:Databases, Genetic; D023281:Genomics / Q000706:statistics & numerical data*; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical; D010802:Phylogeny; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data; D017434:Protein Structure, Tertiary; D012984:Software