MIMPFC: Identifying miRNA-mRNA regulatory modules by combining phase-only correlation and improved rough-fuzzy clustering.
MicroRNAs (miRNAs) play a key role in gene expression and regulation in various organisms. They control a wide range of biological processes and are involved in several types of cancers by causing mRNA degradation or translational inhibition. However, the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With the accumulation of the expression data of miRNAs and mRNAs, many computational methods have been proposed to predict miRNA-mRNA regulatory relationship. However, most existing methods require the number of modules predefined that may be difficult to determine beforehand. Here, we propose a novel computational method to discover miRNA-mRNA regulatory modules by combining Phase-only correlation and improved rough-Fuzzy Clustering (MIMPFC). The proposed method is evaluated on three heterogeneous datasets, and the obtained results are further validated through relevant literatures, biological significance and functional enrichment analysis. The analysis results show that the identified modules are highly correlated with the biological conditions. A large part of the regulatory relationships found by MIMPFC has been confirmed in the experimentally verified databases. It demonstrates that the modules found by MIMPFC are biologically significant.
['Luo|Dan|D|', 'Wang|Shu-Lin|SL|', 'Fang|Jianwen|J|', 'Zhang|Wei|W|']
D000465:Algorithms D001943:Breast Neoplasms D016000:Cluster Analysis D019295:Computational Biology D030541:Databases, Genetic D017143:Fuzzy Logic D063990:Gene Ontology D053263:Gene Regulatory Networks D006801:Humans D035683:MicroRNAs D012333:RNA, Messenger D015203:Reproducibility of Results
clustering gene expression miRNA–mRNA regulatory modules