A sparse Bayesian learning based approach to Inferring Gene Regulatory Networks

Inferring Gene Regulatory Networks (GRNs) from high-throughput experimental data is an important problem in Systems Biology. In this paper we present a new algorithm for the task. Our algorithm is based on a sparse Bayesian learning framework and works well with steady state gene expression data. To evaluate its performance, we compare our algorithm with two state of the art algorithms on multiple synthetic datasets. A comparison of the results shows that our approach is competitive with these two methods on small-sized networks. The paucity and noisy nature of experimental data remains an obstacle to further progress.