A Posterior Probability Approach for Gene Regulatory Network Inference in Genetic Perturbation Data

TL;DR

This paper introduces a Bayesian posterior probability method for inferring gene regulatory networks from perturbation data, effectively identifying known regulatory edges while considering biological variability.

Contribution

The paper presents a novel, efficient Bayesian approach that incorporates prior knowledge to improve gene regulatory network inference from perturbation data.

Findings

Successfully identified known gene regulatory edges

Method outperforms some existing approaches in accuracy

Provides a fast and simple inference technique

Abstract

Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We propose a fast, simple method for inferring regulatory relationships between genes from knockdown experiments in the NIH LINCS dataset by calculating posterior probabilities, incorporating prior information. We show that the method is able to find previously identified edges from TRANSFAC and JASPAR and discuss the merits and limitations of this approach.