Rough sets for in silico identification of differentially expressed miRNAs
Received 26 November 2012
Accepted for publication 19 January 2013
Published 16 September 2013 Volume 2013:8(Supplement 1 Nanoinformatics) Pages 63—74
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
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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