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Issue 7, 2015
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Rough hypercuboid based supervised clustering of miRNAs

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Abstract

The microRNAs are small, endogenous non-coding RNAs found in plants, animals, and some viruses, which function in RNA silencing and post-transcriptional regulation of gene expression. It is suggested by various genome-wide studies that a substantial fraction of miRNA genes is likely to form clusters. The coherent expression of the miRNA clusters can then be used to classify samples according to the clinical outcome. In this regard, a new clustering algorithm, termed as rough hypercuboid based supervised attribute clustering (RH-SAC), is proposed to find such groups of miRNAs. The proposed algorithm is based on the theory of rough set, which directly incorporates the information of sample categories into the miRNA clustering process, generating a supervised clustering algorithm for miRNAs. The effectiveness of the new approach is demonstrated on several publicly available miRNA expression data sets using support vector machine. The so-called B.632+ bootstrap error estimate is used to minimize the variability and biasedness of the derived results. The association of the miRNA clusters to various biological pathways is also shown by doing pathway enrichment analysis.

Graphical abstract: Rough hypercuboid based supervised clustering of miRNAs

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Publication details

The article was received on 26 Mar 2015, accepted on 14 May 2015 and first published on 14 May 2015


Article type: Paper
DOI: 10.1039/C5MB00213C
Citation: Mol. BioSyst., 2015,11, 2068-2081
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    Rough hypercuboid based supervised clustering of miRNAs

    S. Paul and J. Vera, Mol. BioSyst., 2015, 11, 2068
    DOI: 10.1039/C5MB00213C

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