A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.

ABSTRACT: While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi-supervised REgulatory Network Discoverer), a semi-supervised learning method that uses a curated database of verified transcriptional factor-gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor-gene interactions, incl

SUBMITTER: Ernst J 

PROVIDER: S-EPMC2266799 | biostudies-literature | 2008 Mar

REPOSITORIES: biostudies-literature