The Research On Algorithm Of Identifying MiRNA Regulatory Modules Based On Multi-source Data

With the rapid development of biological technology,a large amount of biological data has emerged,which provide a powerful support for the study of the function of biological molecules.How to use these biological data to dig out the valuable information has become an unprecedented challenge for researchers.It has a very important significance to integrate the miRNA-mRNA regulatory interactions and protein-protein interactions to identify the miRNA-mRNA regulatory modules for understanding complex biological systems in molecular combination effect and revealing important miRNAs and target genes which are the causes of complex diseases.Most of methods of identifying miRNA-mRNA modules needed to predefine the number of modules.Therefore,in this study,a new algorithm called MiRMD(miRNA-mRNA regulatory modules detection)is presented to identify miRNA-mRNA regulatory modules.Firstly,a miRNA-mRNA regulatory network is constructed by using miRNA/mRNA expression profiles and the target site information,then core structures are detected in this network by merging cohesive modules.Next,some overlapping neighbor nodes are added into the cores according to the density.Finally,some overlap modules are filtered.The experimental results based on three cancers datasets show that miRNA-mRNA regulatory modules identified by MiRMD are more coherent and functional enriched than the other two methods according to MiMEC(miRNA-mRNA expression correlation)and GO enrichment.Particularly,modules that our method identified are strongly implicated in cancer.The algorithm MiRMD started from a module which contains a single miRNA and some target mRNAs to detect the core structures,then based on the overlapping neighbor expansion to form the final miRNA regulatory modules,however,which did not consider the collective group relationships between a group of miRNAs and a group of mRNAs.A method called CGR is proposed to discover miRNA-mRNA regulatory modules and reveal miRNA-mRNA regulatory relationships from the heterogeneous expression data based on the collective relationships.Fistly,a miRNA-miRNA synergy network is constructed according to the edge weight of miRNA-mRNA regulatory network,then some miRNA clusters are identified in the synergy network,next several mRNA clusters are identified in the protein-protein interaction network,finally the miRNA clusters and mRNA clusters are merged to form miRNA-mRNA regulatory modules according to the regulatory relationships.The experiments on three data sets prove that the miRNA regulatory modules identified by CGR can get better effect.