Inferring Small-Scale Maximum-Entropy Genetic Regulatory Networks by Using DE Algorithm

Fu Yin, Jiarui Zhou, Zexuan Zhu, Xiaoliang Ma, Weixin Xie

Abstract: Maximum-entropy genetic regulatory networks (GRNs) have been increasingly applied to infer pairwise gene interactions from biological data. Most maximum-entropy GRNs inferring methods estimate the inverse covariance matrix based on the assumption that the network is sparse and the problem can be approximated via convex optimization. However, the assumption might not be true in reality. To address this issue, in this paper, we propose an adaptive differential evolution (DE) algorithm to directly infer the maximum-entropy GRNs, which is formulated as a constrained optimization problem with the maximum entropy being the objective function and the first and second moments being two penalty terms. A GRN inferred by DE is a fully connected network that can reflect the gene regulatory relations. The experimental results on both simulated and real data suggest that the proposed method is robust in inferring the small-scale maximum-entropy GRNs.