Transcription profiling of E. coli under 121 conditions to generate a compendium of gene expression

ABSTRACT: Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of

ORGANISM(S): Escherichia coli

SUBMITTER: Timothy Gardner 

PROVIDER: E-GEOD-6836 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress