Optimization based tumor classification from microarray gene expression data.

<h4>Background</h4>An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algo...

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Autores principales:	Onur Dagliyan, Fadime Uney-Yuksektepe, I Halil Kavakli, Metin Turkay
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8c2a87a5c48b446aadcaf6de57ab9676
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spelling	oai:doaj.org-article:8c2a87a5c48b446aadcaf6de57ab96762021-11-18T06:59:13ZOptimization based tumor classification from microarray gene expression data.1932-620310.1371/journal.pone.0014579https://doaj.org/article/8c2a87a5c48b446aadcaf6de57ab96762011-02-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21326602/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types.<h4>Methodology/principal findings</h4>We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described.<h4>Conclusions/significance</h4>The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers.Onur DagliyanFadime Uney-YuksektepeI Halil KavakliMetin TurkayPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 2, p e14579 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Onur Dagliyan Fadime Uney-Yuksektepe I Halil Kavakli Metin Turkay Optimization based tumor classification from microarray gene expression data.
description	<h4>Background</h4>An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types.<h4>Methodology/principal findings</h4>We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described.<h4>Conclusions/significance</h4>The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers.
format	article
author	Onur Dagliyan Fadime Uney-Yuksektepe I Halil Kavakli Metin Turkay
author_facet	Onur Dagliyan Fadime Uney-Yuksektepe I Halil Kavakli Metin Turkay
author_sort	Onur Dagliyan
title	Optimization based tumor classification from microarray gene expression data.
title_short	Optimization based tumor classification from microarray gene expression data.
title_full	Optimization based tumor classification from microarray gene expression data.
title_fullStr	Optimization based tumor classification from microarray gene expression data.
title_full_unstemmed	Optimization based tumor classification from microarray gene expression data.
title_sort	optimization based tumor classification from microarray gene expression data.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/8c2a87a5c48b446aadcaf6de57ab9676
work_keys_str_mv	AT onurdagliyan optimizationbasedtumorclassificationfrommicroarraygeneexpressiondata AT fadimeuneyyuksektepe optimizationbasedtumorclassificationfrommicroarraygeneexpressiondata AT ihalilkavakli optimizationbasedtumorclassificationfrommicroarraygeneexpressiondata AT metinturkay optimizationbasedtumorclassificationfrommicroarraygeneexpressiondata
_version_	1718424117292564480

Optimization based tumor classification from microarray gene expression data.

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