Predicting transcriptional regulatory interactions with artificial neural networks applied to E. coli multidrug resistance efflux pumps.

ABSTRACT:

Background

Little is known about bacterial transcriptional regulatory networks (TRNs). In Escherichia coli, which is the organism with the largest wet-lab validated TRN, its set of interactions involves only approximately 50% of the repertoire of transcription factors currently known, and ~25% of its genes. Of those, only a small proportion describes the regulation of processes that are clinically relevant, such as drug resistance mechanisms.

Results

We designed feed-forward (FF) and bi-fan (BF) motif predictors for E. coli using multi-layer perceptron artificial neural networks (ANNs). The motif predictors were trained using a large dataset of gene expression data; the collection of motifs was extracted from the E. coli TRN. Each network motif was mapped to a vector of corr

SUBMITTER: Veiga DF 

PROVIDER: S-EPMC2453137 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature