Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

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…

• NS
  National Science Foundation

  Awards: EF-0425719, 0425719
• UD
  U.S. Department of Energy

  Awards: FG02-04ER63803, DE-FG02-, DE-FG02
• PR
  Pharmaceutical Research and Manufacturers of America Foundation
• NI
  National Institutes of Health
• OO
  Office of Science