Back to Journals » International Journal of Nanomedicine » Volume 8 » Supplement 1 Nanoinformatics

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


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

Creative Commons License © 2013 The Author(s). This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.