Inference algorithms for gene networks: a statistical mechanics analysis

TL;DR

This paper compares two algorithms for inferring gene regulatory networks from expression data, showing that message-passing techniques outperform correlation-based methods due to their ability to consider collective effects.

Contribution

It introduces a statistical mechanics framework to analyze and compare the performance of correlation-based and message-passing algorithms in gene network inference.

Findings

Message-passing algorithms outperform correlation-based methods.

Theoretical analysis uses the replica method from statistical physics.

Collective effects of multiple regulators improve inference accuracy.

Abstract

The inference of gene regulatory networks from high throughput gene expression data is one of the major challenges in systems biology. This paper aims at analysing and comparing two different algorithmic approaches. The first approach uses pairwise correlations between regulated and regulating genes; the second one uses message-passing techniques for inferring activating and inhibiting regulatory interactions. The performance of these two algorithms can be analysed theoretically on well-defined test sets, using tools from the statistical physics of disordered systems like the replica method. We find that the second algorithm outperforms the first one since it takes into account collective effects of multiple regulators.