Study on Gene Regulatory Network Inference Using PLS-based Methods

Abstract

We introduce an ensemble gene regulatory network inference method PLSNET, which decomposes the GRN inference problem with p genes into p subproblems and solves each of the subproblems by using Partial least squares (PLS) based feature selection algorithm. Numerous potential uses for inferring the topology of gene regulatory networks (GRNs) from microarray gene expression data include identifying potential drug targets and offering important insights into biological processes. The data is noisy, high dimensional, and there are many potential interactions, so it continues to be a challenge. Then, a statistical technique is used to refine the predictions in our method. The proposed method was evaluated on the DREAM4 and DREAM5 benchmark datasets and achieved higher accuracy than the winners of those competitions and other state-of-the-art GRN inference methods.

Superior accuracy achieved on different benchmark datasets, including both in silico and in vivo networks, shows that PLSNET reaches state-of-the-art performance.