Identification of yeast transcriptional regulation networks using multivariate random forests.

ABSTRACT: The recent availability of whole-genome scale data sets that investigate complementary and diverse aspects of transcriptional regulation has spawned an increased need for new and effective computational approaches to analyze and integrate these large scale assays. Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays) to sequence features residing in gene promoters (as derived from DNA motif data) and transcription factor binding to gene promoters (as derived from tiling microarrays). We extend the random forest approach to model a multivariate response as represented, for example, by time-course gene expression measures. An analysis of the multivariate random forest output reveals complex regulatory networ

SUBMITTER: Xiao Y 

PROVIDER: S-EPMC2691601 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature