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Inferring gene regulatory networks is an important problem in systemsbiology. However, these networks can be hard to infer from experimental databecause of the inherent variability in biological data as well as the largenumber of genes involved. We propose a fast, simple method for inferringregulatory relationships between genes from knockdown experiments in the NIHLINCS dataset by calculating posterior probabilities, incorporating priorinformation. We show that the method is able to find previously identifiededges from TRANSFAC and JASPAR and discuss the merits and limitations of thisapproach.

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