Abstract
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.
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