SIRENE: supervised inference of regulatory networks

Motivation: Living cells are the product of gene expression programs that involve the regulated transcription of thousands of genes. The elucidation of transcriptional regulatory networks is thus needed to understand the cells working mechanism, and can for example, be useful for the discovery of novel therapeutic targets. Although several methods have been proposed to infer gene regulatory networks from gene expression data, a recent comparison on a large-scale benchmark experiment revealed that most current methods only predict a limited number of known regulations at a reasonable precision level. Results: We propose SIRENE (Supervised Inference of Regulatory Networks), a new method for the inference of gene regulatory networks from a compendium of expression data. The method decomposes the problem of gene regulatory network inference into a large number of local binary classification problems, that focus on separating target genes from non-targets for each transcription factor. SIRENE is thus conceptually simple and computationally efficient. We test it on a benchmark experiment aimed at predicting regulations in Escherichia coli, and show that it retrieves of the order of 6 times more known regulations than other state-of-the-art inference methods.