Abstract: With the development of high-throughput technology, more and more biomedical data need to be processed and analyzed. Bioinformatics is one of the fundamental ways to effectively analyze high-dimensional biomedical data. This paper is to provide a brief review of our recent works in gene regulatory network inference. According to different types of transcriptomic data and research purposes, the corresponding network inference methods were established, including the establishment of a priori gene regulatory network database, causal network inference based on conditional mutual information, dynamic gene regulation network inference based on differential equations, transcriptional regulation and post-transcriptional regulation inference, and the evaluation of gene regulatory network activity. At the same time, the important research directions in gene regulatory network inference are prospected.

Key words: gene regulatory network inference, causality inference, probabilistic graphical model, combinatorial optimization, operations research and bioinformatics