SIRENE: Supervised Inference of Regulatory Networks

TL;DR

SIRENE is a new, efficient method for inferring gene regulatory networks from expression data, significantly outperforming existing methods by predicting more known regulations in E. coli.

Contribution

It introduces a local binary classification approach for network inference, improving prediction accuracy and computational efficiency over prior methods.

Findings

Retrieves approximately six times more known regulations than existing methods.

Demonstrates high accuracy in predicting E. coli gene regulations.

Proves computational efficiency of the local classification approach.

Abstract

Living cells are the product of gene expression programs that involve the regulated transcription of thousands of genes. The elucidation of transcriptional regulatory networks in thus needed to understand the cell's 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. We propose SIRENE, 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 TF. SIRENE is thus conceptually simple and computationally efficient. We test it on a benchmark experiment aimed at predicting regulations in E. coli, and show that it retrieves of the order of 6 times more known regulations than other state-of-the-art inference methods.