Paper Info
Title
MIMPFC: Identifying miRNA-mRNA regulatory modules by combining phase-only correlation and improved rough-fuzzy clustering.
Abstract
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.
Year
DOI
Venue
2018
10.1142/S0219720017500287
JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
miRNA-mRNA regulatory modules,gene expression,clustering
Fuzzy clustering,Biology,microRNA,Phase only correlation,Gene expression,Messenger RNA,Artificial intelligence,Computational biology,Cluster analysis,Machine learning
Journal
Volume
Issue
ISSN
16
SP1
0219-7200
Citations 
PageRank 
References 
0
0.34
9
Authors
4
Name
Order
Citations
PageRank
Dan Luo143.17
Shulin Wang2277.13
Jianwen Fang314814.82
Wei Zhang428735.43