A probabilistic generative model for GO enrichment analysis.

ABSTRACT: The Gene Ontology (GO) is extensively used to analyze all types of high-throughput experiments. However, researchers still face several challenges when using GO and other functional annotation databases. One problem is the large number of multiple hypotheses that are being tested for each study. In addition, categories often overlap with both direct parents/descendents and other distant categories in the hierarchical structure. This makes it hard to determine if the identified significant categories represent different functional outcomes or rather a redundant view of the same biological processes. To overcome these problems we developed a generative probabilistic model which identifies a (small) subset of categories that, together, explain the selected gene set. Our model accommodates noi

SUBMITTER: Lu Y 

PROVIDER: S-EPMC2553574 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature