Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model.

ABSTRACT:

Background

Large-scale genomic studies often identify large gene lists, for example, the genes sharing the same expression patterns. The interpretation of these gene lists is generally achieved by extracting concepts overrepresented in the gene lists. This analysis often depends on manual annotation of genes based on controlled vocabularies, in particular, Gene Ontology (GO). However, the annotation of genes is a labor-intensive process; and the vocabularies are generally incomplete, leaving some important biological domains inadequately covered.

Results

We propose a statistical method that uses the primary literature, i.e. free-text, as the source to perform overrepresentation analysis. The method is based on a statistical framework of mixture model and addresses the method

SUBMITTER: He X 

PROVIDER: S-EPMC2885378 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature