Grouping miRNAs of similar functions via weighted information content of gene ontology.
BACKGROUND
Regulation mechanisms between miRNAs and genes are complicated. To accomplish a biological function, a miRNA may regulate multiple target genes, and similarly a target gene may be regulated by multiple miRNAs. Wet-lab knowledge of co-regulating miRNAs is limited. This work introduces a computational method to group miRNAs of similar functions to identify co-regulating miRNAsfrom a similarity matrix of miRNAs.


RESULTS
We define a novel information content of gene ontology (GO) to measure similarity between two sets of GO graphs corresponding to the two sets of target genes of two miRNAs. This between-graph similarity is then transferred as a functional similarity between the two miRNAs. Our definition of the information content is based on the size of a GO term's descendants, but adjusted by a weight derived from its depth level and the GO relationships at its path to the root node or to the most informative common ancestor (MICA). Further, a self-tuning technique and the eigenvalues of the normalized Laplacian matrix are applied to determine the optimal parameters for the spectral clustering of the similarity matrix of the miRNAs.


CONCLUSIONS
Experimental results demonstrate that our method has better clustering performance than the existing edge-based, node-based or hybrid methods. Our method has also demonstrated a novel usefulness for the function annotation of new miRNAs, as reported in the detailed case studies.
School of Computer, Electronic and Information, and State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi University, No.100 Daxue Road, Nanning, 530004, China.|Advanced Analytics Institute, Faculty of Engineering and IT, University of Technology Sydney, PO Box 123, Broadway, Sydney, NSW 2007, Australia.
Chaowang
C
Lan
School of Computer, Electronic and Information, and State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi University, No.100 Daxue Road, Nanning, 530004, China. qingfeng@gxu.edu.cn.
Qingfeng
Q
Chen
Advanced Analytics Institute, Faculty of Engineering and IT, University of Technology Sydney, PO Box 123, Broadway, Sydney, NSW 2007, Australia. jinyan.li@uts.edu.au.
Jinyan
J
Li
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D063990:Gene Ontology; D006801:Humans; D035683:MicroRNAs / Q000235:genetics*; D015233:Models, Statistical
D035683:MicroRNAs
Functions of miRNAs; GO graphs; Gene ontology; Information content; Spectral clustering