KtreeGRN: A Method of Gene Regulatory Network Construction Based on k-tree Sampling and Decomposition

Abstract

How to construct accurate gene regulatory networks $(GRN)$ has important significance for the research of functional genomics. Several existing gene regulatory network construction methods have the problems of low accuracy and unable to handle large scale network effectively. In order to solve the above problems, this work proposes a gene regulatory network construction method based on k-tree sampling and decomposition (KtreeGRN). It transforms the problem of gene network construction into generating codes. Firstly, it constructs the k-tree structure of the gene regulatory network through sampling dandelion codes uniformly. Then, the k-tree is decomposed into several k-cliques using the tree decomposition algorithm based on the minimum degree selection. It constructs the sub-networks for the genes in each k-clique using the mixed entropy optimizing mutual information method. Finally, it obtains the whole gene regulatory network through merging all the sub-networks. It repeats the above operations (k-tree generation, k-tree decomposition, network generation) several times and gets the final gene regulatory network according to the frequency of edges. Experimental results show that KtreeGRN performs better than other several kinds of gene network construction methods on the simulated dataset of DREAM challenge and two real datasets of Escherichia-coil (E. coli SOS pathway network, E. coli SOS DNA repair network).