PMID:18613947
| Citation |
Guan, Y, Myers, CL, Hess, DC, Barutcuoglu, Z, Caudy, AA and Troyanskaya, OG (2008) Predicting gene function in a hierarchical context with an ensemble of classifiers. Genome Biol. 9 Suppl 1:S3 |
|---|---|
| Abstract |
The wide availability of genome-scale data for several organisms has stimulated interest in computational approaches to gene function prediction. Diverse machine learning methods have been applied to unicellular organisms with some success, but few have been extensively tested on higher level, multicellular organisms. A recent mouse function prediction project (MouseFunc) brought together nine bioinformatics teams applying a diverse array of methodologies to mount the first large-scale effort to predict gene function in the laboratory mouse. |
| Links |
PubMed PMC2447537 Online version:10.1186/gb-2008-9-s1-s3 |
| Keywords |
Algorithms; Animals; Bayes Theorem; Mice/genetics; Mice/metabolism; Mitochondrial Proteins/genetics; Mitochondrial Proteins/metabolism; Proteins/genetics; Proteins/metabolism; Saccharomyces cerevisiae/genetics; Saccharomyces cerevisiae Proteins/genetics; Saccharomyces cerevisiae Proteins/metabolism |
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