Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm

• Published in: Interdisciplinary sciences : computational life sciences Vol. 14; no. 1; pp. 168 - 181
• Main Authors: Liu, Wei, Jiang, Yi, Peng, Li, Sun, Xingen, Gan, Wenqing, Zhao, Qi, Tang, Huanrong
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.03.2022; Springer Nature B.V
• Subjects: Algorithms; Biomedical and Life Sciences; Computational Biology - methods; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Computer applications; Data processing; DNA microarrays; Gene Regulatory Networks; Genes; Health Sciences; Life Sciences; Mathematical and Computational Physics; Medicine; Original Research Article; Statistics for Life Sciences; Theoretical; Theoretical and Computational Chemistry; Markov blanket; Gene regulatory networks; Feature selection; Data processing inequality
• ISSN: 1913-2751; 1867-1462; 1867-1462
• DOI: 10.1007/s12539-021-00478-9

Inferring gene regulatory networks (GRNs) from microarray data can help us understand the mechanisms of life and eventually develop effective therapies. Currently, many computational methods have been used in inferring GRNs. However, owing to high-dimensional data and small samples, these methods often tend to introduce redundant regulatory relationships. Therefore, a novel network inference method based on the improved Markov blanket discovery algorithm, IMBDANET, is proposed to infer GRNs. Specifically, for each target gene, data processing inequality was applied to the Markov blanket discovery algorithm for the accurate differentiation of direct regulatory genes from indirect regulatory genes. Finally, direct regulatory genes were used in constructing GRNs, and the network structure was optimized according to the importance degree score. Experimental results on six public network datasets show that the proposed method can be effectively used to infer GRNs. Graphic abstract