Weighted-Lasso for Structured Network Inference from Time Course Data

TL;DR

This paper introduces a weighted-Lasso approach for inferring gene regulatory networks from time course data, leveraging prior biological knowledge or data-driven structure to improve inference accuracy.

Contribution

It proposes a novel weighted-Lasso method that incorporates network structure into the inference of vector auto-regressive models for gene regulation.

Findings

Effective on synthetic data and real biological networks

Improves network inference accuracy with prior structure

Validated on yeast and E. coli datasets

Abstract

We present a weighted-Lasso method to infer the parameters of a first-order vector auto-regressive model that describes time course expression data generated by directed gene-to-gene regulation networks. These networks are assumed to own a prior internal structure of connectivity which drives the inference method. This prior structure can be either derived from prior biological knowledge or inferred by the method itself. We illustrate the performance of this structure-based penalization both on synthetic data and on two canonical regulatory networks, first yeast cell cycle regulation network by analyzing Spellman et al's dataset and second E. coli S.O.S. DNA repair network by analysing U. Alon's lab data.