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Rough sets for in silico identification of differentially expressed miRNAs

Authors Paul S, Maji P

Received 26 November 2012

Accepted for publication 19 January 2013

Published 16 September 2013 Volume 2013:8(Supplement 1 Nanoinformatics) Pages 63—74

DOI https://doi.org/10.2147/IJN.S40739

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Sushmita Paul, Pradipta Maji

Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

Abstract: The microRNAs, also known as miRNAs, are the class of small noncoding RNAs. They repress the expression of a gene posttranscriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and the utility of miRNAs for the diagnosis of cancer and other diseases. Unlike with mRNAs, a modest number of miRNAs might be sufficient to classify human cancers. However, the absence of a robust method to identify differentially expressed miRNAs makes this an open problem. In this regard, this paper presents a novel approach for in silico identification of differentially expressed miRNAs from microarray expression data sets. It integrates judiciously the theory of rough sets and merit of the so-called B.632+ bootstrap error estimate. While rough sets select relevant and significant miRNAs from expression data, the B.632+ error rate minimizes the variability and bias of the derived results. The effectiveness of the proposed approach, along with a comparison with other related approaches, is demonstrated on several miRNA microarray expression data sets, using the support vector machine.

Keywords: microRNA, feature selection, bootstrap error, support vector machine

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