Inferring mouse gene functions from genomic-scale data using a combined functional network/classification strategy.

ABSTRACT: The complete set of mouse genes, as with the set of human genes, is still largely uncharacterized, with many pieces of experimental evidence accumulating regarding the activities and expression of the genes, but the majority of genes as yet still of unknown function. Within the context of the MouseFunc competition, we developed and applied two distinct large-scale data mining approaches to infer the functions (Gene Ontology annotations) of mouse genes from experimental observations from available functional genomics, proteomics, comparative genomics, and phenotypic data. The two strategies - the first using classifiers to map features to annotations, the second propagating annotations from characterized genes to uncharacterized genes along edges in a network constructed from the features -

SUBMITTER: Kim WK 

PROVIDER: S-EPMC2447539 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature