Selecting differentially expressed genes for colon tumor classification

Krzysztof Fujarewicz; Małgorzata Wiench

International Journal of Applied Mathematics and Computer Science (2003)

  • Volume: 13, Issue: 3, page 327-335
  • ISSN: 1641-876X

Abstract

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DNA microarrays provide a new technique of measuring gene expression, which has attracted a lot of research interest in recent years. It was suggested that gene expression data from microarrays (biochips) can be employed in many biomedical areas, e.g., in cancer classification. Although several, new and existing, methods of classification were tested, a selection of proper (optimal) set of genes, the expressions of which can serve during classification, is still an open problem. Recently we have proposed a new recursive feature replacement (RFR) algorithm for choosing a suboptimal set of genes. The algorithm uses the support vector machines (SVM) technique. In this paper we use the RFR method for finding suboptimal gene subsets for tumornormal colon tissue classification. The obtained results are compared with the results of applying other methods recently proposed in the literature. The comparison shows that the RFR method is able to find the smallest gene subset (only six genes) that gives no misclassifications in leave-one-out cross-validation for a tumornormal colon data set. In this sense the RFR algorithm outperforms all other investigated methods.

How to cite

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Fujarewicz, Krzysztof, and Wiench, Małgorzata. "Selecting differentially expressed genes for colon tumor classification." International Journal of Applied Mathematics and Computer Science 13.3 (2003): 327-335. <http://eudml.org/doc/207647>.

@article{Fujarewicz2003,
abstract = {DNA microarrays provide a new technique of measuring gene expression, which has attracted a lot of research interest in recent years. It was suggested that gene expression data from microarrays (biochips) can be employed in many biomedical areas, e.g., in cancer classification. Although several, new and existing, methods of classification were tested, a selection of proper (optimal) set of genes, the expressions of which can serve during classification, is still an open problem. Recently we have proposed a new recursive feature replacement (RFR) algorithm for choosing a suboptimal set of genes. The algorithm uses the support vector machines (SVM) technique. In this paper we use the RFR method for finding suboptimal gene subsets for tumornormal colon tissue classification. The obtained results are compared with the results of applying other methods recently proposed in the literature. The comparison shows that the RFR method is able to find the smallest gene subset (only six genes) that gives no misclassifications in leave-one-out cross-validation for a tumornormal colon data set. In this sense the RFR algorithm outperforms all other investigated methods.},
author = {Fujarewicz, Krzysztof, Wiench, Małgorzata},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {feature selection; support vector machines; colon tumor; gene expression data; microarrays; classification},
language = {eng},
number = {3},
pages = {327-335},
title = {Selecting differentially expressed genes for colon tumor classification},
url = {http://eudml.org/doc/207647},
volume = {13},
year = {2003},
}

TY - JOUR
AU - Fujarewicz, Krzysztof
AU - Wiench, Małgorzata
TI - Selecting differentially expressed genes for colon tumor classification
JO - International Journal of Applied Mathematics and Computer Science
PY - 2003
VL - 13
IS - 3
SP - 327
EP - 335
AB - DNA microarrays provide a new technique of measuring gene expression, which has attracted a lot of research interest in recent years. It was suggested that gene expression data from microarrays (biochips) can be employed in many biomedical areas, e.g., in cancer classification. Although several, new and existing, methods of classification were tested, a selection of proper (optimal) set of genes, the expressions of which can serve during classification, is still an open problem. Recently we have proposed a new recursive feature replacement (RFR) algorithm for choosing a suboptimal set of genes. The algorithm uses the support vector machines (SVM) technique. In this paper we use the RFR method for finding suboptimal gene subsets for tumornormal colon tissue classification. The obtained results are compared with the results of applying other methods recently proposed in the literature. The comparison shows that the RFR method is able to find the smallest gene subset (only six genes) that gives no misclassifications in leave-one-out cross-validation for a tumornormal colon data set. In this sense the RFR algorithm outperforms all other investigated methods.
LA - eng
KW - feature selection; support vector machines; colon tumor; gene expression data; microarrays; classification
UR - http://eudml.org/doc/207647
ER -

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