Predicting gene ontology biological process from temporal gene expression patterns.

ABSTRACT: The aim of the present study was to generate hypotheses on the involvement of uncharacterized genes in biological processes. To this end, supervised learning was used to analyze microarray-derived time-series gene expression data. Our method was objectively evaluated on known genes using cross-validation and provided high-precision Gene Ontology biological process classifications for 211 of the 213 uncharacterized genes in the data set used. In addition, new roles in biological process were hypothesized for known genes. Our method uses biological knowledge expressed by Gene Ontology and generates a rule model associating this knowledge with minimal characteristic features of temporal gene expression profiles. This model allows learning and classification of multiple biological process role

SUBMITTER: Lagreid A 

PROVIDER: S-EPMC430886 | biostudies-literature | 2003 May

REPOSITORIES: biostudies-literature