Rough sets for in silico identification of differentially expressed miRNAs

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 pro...

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Autores principales:	Paul S, Maji P
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2013
Materias:	Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/d0554582e7374d6fbf1cc12a504a0072
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spelling	oai:doaj.org-article:d0554582e7374d6fbf1cc12a504a00722021-12-02T02:58:58ZRough sets for in silico identification of differentially expressed miRNAs1176-91141178-2013https://doaj.org/article/d0554582e7374d6fbf1cc12a504a00722013-09-01T00:00:00Zhttp://www.dovepress.com/rough-sets-for-in-silico-identification-of-differentially-expressed-mi-a14363https://doaj.org/toc/1176-9114https://doaj.org/toc/1178-2013Sushmita 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 machinePaul SMaji PDove Medical PressarticleMedicine (General)R5-920ENInternational Journal of Nanomedicine, Vol 2013, Iss Supplement 1 Nanoinformatics, Pp 63-74 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920
spellingShingle	Medicine (General) R5-920 Paul S Maji P Rough sets for in silico identification of differentially expressed miRNAs
description	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
format	article
author	Paul S Maji P
author_facet	Paul S Maji P
author_sort	Paul S
title	Rough sets for in silico identification of differentially expressed miRNAs
title_short	Rough sets for in silico identification of differentially expressed miRNAs
title_full	Rough sets for in silico identification of differentially expressed miRNAs
title_fullStr	Rough sets for in silico identification of differentially expressed miRNAs
title_full_unstemmed	Rough sets for in silico identification of differentially expressed miRNAs
title_sort	rough sets for in silico identification of differentially expressed mirnas
publisher	Dove Medical Press
publishDate	2013
url	https://doaj.org/article/d0554582e7374d6fbf1cc12a504a0072
work_keys_str_mv	AT pauls roughsetsforinsilicoidentificationofdifferentiallyexpressedmirnas AT majip roughsetsforinsilicoidentificationofdifferentiallyexpressedmirnas
_version_	1718402070026911744

Rough sets for in silico identification of differentially expressed miRNAs

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