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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 |
Appears in Collections: | Elements Staff Publications |
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