Classifying transcription factor targets and discovering relevant biological features.

ABSTRACT:

Background

An important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We have previously reported the development of a supervised-learning approach to TF target identification, and used it to predict targets of 104 transcription factors in yeast. We now include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties.

Principal findings

(1) Applic

SUBMITTER: Holloway DT 

PROVIDER: S-EPMC2441612 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature