Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12859-016-1367-0
Title: Grouping miRNAs of similar functions via weighted information content of gene ontology
Authors: Lan, C
Chen, Q 
Li, J
Keywords: Clustering algorithms
Eigenvalues and eigenfunctions
Genes
Matrix algebra
Mica
Silicate minerals
Functional similarity
Gene ontology
GO graphs
Information contents
Normalized Laplacian
Regulation mechanisms
Self-tuning technique
Spectral clustering
RNA
microRNA
algorithm
biology
cluster analysis
gene expression profiling
gene ontology
genetics
human
procedures
statistical model
Algorithms
Cluster Analysis
Computational Biology
Gene Expression Profiling
Gene Ontology
Humans
MicroRNAs
Models, Statistical
Issue Date: 2016
Citation: Lan, C, Chen, Q, Li, J (2016). Grouping miRNAs of similar functions via weighted information content of gene ontology. BMC Bioinformatics 17 : 507. ScholarBank@NUS Repository. https://doi.org/10.1186/s12859-016-1367-0
Rights: Attribution 4.0 International
Abstract: 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. © 2016 The Author(s).
Source Title: BMC Bioinformatics
URI: https://scholarbank.nus.edu.sg/handle/10635/181315
ISSN: 14712105
DOI: 10.1186/s12859-016-1367-0
Rights: Attribution 4.0 International
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