An approach of gene regulatory network construction using mixed entropy optimizing context-related likelihood mutual information

Abstract

Motivation The question of how to construct gene regulatory networks has long been a focus of biological research. Mutual information can be used to measure nonlinear relationships, and it has been widely used in the construction of gene regulatory networks. However, this method cannot measure indirect regulatory relationships under the influence of multiple genes, which reduces the accuracy of inferring gene regulatory networks. Approach This work proposes a method for constructing gene regulatory networks based on mixed entropy optimizing context-related likelihood mutual information (MEOMI). First, two entropy estimators were combined to calculate the mutual information between genes. Then, distribution optimization was performed using a context-related likelihood algorithm to eliminate some indirect regulatory relationships and obtain the initial gene regulatory network. To obtain the complex interaction between genes and eliminate redundant edges in the network, the initial gene regulatory network was further optimized by calculating the conditional mutual inclusive information (CMI2) between gene pairs under the influence of multiple genes. The network was iteratively updated to reduce the impact of mutual information on the overestimation of the direct regulatory intensity. Results The experimental results show that the MEOMI method performed better than several other kinds of gene network construction methods on DREAM challenge simulated datasets (DREAM3 and DREAM5), three real Escherichia coli datasets (E. coli SOS pathway network, E. coli SOS DNA repair network, and E. coli community network) and two human datasets. Availability and implementation Source code and dataset are available at https://github.com/Dalei-Dalei/MEOMI/ and http://122.205.95.139/MEOMI/. Supplementary information Supplementary data are available at Bioinformatics online.

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks

“…Moreover, the MI is incapable of determining indirect correlations, leading to increased levels of false positives. [70] On the other hand, regression-based methods naturally infer directionality in GRNs. [43][44][45][46] These methods model gene expression using multiple regression models, treating the expression of each gene as the dependent variable, influenced by the expression of other genes as independent variables.…”

“…Moreover, the MI is incapable of determining indirect correlations, leading to increased levels of false positives. [ 70 ]…”

PGBTR: A powerful and general method for inferring bacterial transcriptional regulatory networks

“…Unsupervised learning methods can be further divided into information-based and model-based methods. Information-based methods determine whether there is a regulatory relationship by calculating correlation indicators between gene expressions [9, 10]. Model-based methods build a model that can accurately describe the relationship between genes by fitting gene expression data.…”