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Background:  Individual researchers are struggling to keep up with the accelerating emergence of high-throughput biological data, and to extract information that relates to their specific questions. Integration of accumulated evidence should permit researchers to form fewer - and more accurate - hypotheses for further study through experimentation.    Results:  Here a method previously used to predict Gene Ontology (GO) terms for  Saccharomyces cerevisiae  (Tian  et al .: Combining guilt-by-association and guilt-by-profiling to predict  Saccharomyces cerevisiae  gene function.  Genome Biol  2008, 9(Suppl 1):S7) is applied to predict GO terms and phenotypes for 21,603  Mus musculus  genes, using a diverse collection of integrated data sources (including expression, interaction, and sequence-based data). This combined 'guilt-by-profiling' and 'guilt-by-association' approach optimizes the combination of two inference methodologies. Predictions at all levels of confidence are evaluated by examining genes not used in training, and top predictions are examined manually using available literature and knowledge base resources.    Conclusion:  We assigned a confidence score to each gene/term combination. The results provided high prediction performance, with nearly every GO term achieving greater than 40% precision at 1% recall. Among the 36 novel predictions for GO terms and 40 for phenotypes that were studied manually, >80% and >40%, respectively, were identified as accurate. We also illustrate that a combination of 'guilt-by-profiling' and 'guilt-by-association' outperforms either approach alone in their application to  M. musculus .

An en masse phenotype and function prediction system for Mus musculus