Computational Methods for Inferring and Reconstructing Gene Regulatory Networks

Abstract

Gene regulatory network (GRN) describes dynamic and complex gene interactions that determine the functions of cells. Ergo, understanding of GRNs has played a great role in cancer treatment, drug design, and gene research. However, the GRN study suffers from a lack of experiment measurement data and the complexity of the model. Computational approaches have evolved in recent years to promote the study of GRN. In this review, we summarized popular Computational approaches for GRN reconstruction. We started with traditional bioinformatic methods, such as Bayesian networks and mutual information methods. Later, we introduced how today's hot technology in the computer field - machine learning benefited GRN research. Tree-based approaches and other machine-learning methods are elaborated on in this section. We discussed not only the advantages and progression brought by various methods but also the drawbacks and limitations, such as the accuracy and robustness of GRN reconstruction. We expect to inspire readers for improved GRN study approaches via our introduction to this field.