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Gene Regulatory Network (GRN) inference using computational approaches has been a highly pursued problem in bioinformatics. Various approaches have been developed to infer GRNs from gene expression data including statistical, machine learning and information theoretic approaches. However, a large number of regulatory relationships remain unpredicted even in the highly studied model organisms such as Escherichia coli and Saccharomyces cerevisiae. Besides, the regulatory relationships in higher eukaryotes with large genome sizes, such as humans and mice remain mostly unexplored. Majority of the approaches proposed in the literature on GRNs infer molecular interactions from gene expression data alone, despite the fact that gene expression regulation being a product of sequential interactions of multiple biological processes. To capture more regulatory relationships with higher precision, we apply a data fusion and inference model based on Non-negative Matrix Tri-factorization called integrative matrix tri-factorization for GRN inference (iMTF-GRN) that can integrate the diverse type of biological data in a relational learning framework. We, demonstrate that iMTF-GRN model shows improved accuracy in predicting TF-target and miRNA-target gene regulations and performs comparatively better over other state-of-the-art methods.

iMTF-GRN: Integrative Matrix Tri-Factorization for Inference of Gene Regulatory Networks