Reverse Engineering Gene Regulatory Networks Based on Dynamic Threshold Condition Mutual Information With Resampling Strategy

Recently, constructing relatively accurate gene regulatory networks has become a hot research direction in the field of bioinformatics. Path consistency algorithm based on conditional mutual information (PCACMI) is a practical algorithm to reconstruct gene regulation networks, but the threshold is fixed, which will affect the accuracy of the reconstructed networks. So we improve PCACMI and design a new algorithm termed dynamic threshold condition mutual information (DTCMI). In the new algorithm, the threshold is related to the maximal element of the weight matrix of different orders, and the value of threshold will change with maximal weights. In addition, in order to improve the accuracy of the reconstructed network, we firstly employ resampling strategy by utilizing the jackknife to deal with the gene expression data. Finally, we reconstruct the networks by using gene knock-out expression data from the stochastic differential equation and DREAM4 challenges. The results show that the performance of DTCMI is more effective.