Geodesic
Selecting Differentially Expressed Genes for Colon Tumor Classification
International Journal of Applied Mathematics and Computer Science, Tome 13 (2003) no. 3, pp. 327-335

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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 tumor/normal 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 tumor/normal colon data set. In this sense the RFR algorithm outperforms all other investigated methods.
Keywords: colon tumor, gene expression data, microarrays, support vector machines, feature selection, classification
Mots-clés : medycyna, automatyka 
@article{IJAMCS_2003_13_3_a5,
     author = {Fujarewicz, K. and Wiench, M.},
     title = {Selecting {Differentially} {Expressed} {Genes} for {Colon} {Tumor} {Classification}},
     journal = {International Journal of Applied Mathematics and Computer Science},
     pages = {327--335},
     publisher = {mathdoc},
     volume = {13},
     number = {3},
     year = {2003},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/IJAMCS_2003_13_3_a5/}
}
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Fujarewicz, K.; Wiench, M. Selecting Differentially Expressed Genes for Colon Tumor Classification. International Journal of Applied Mathematics and Computer Science, Tome 13 (2003) no. 3, pp. 327-335. http://geodesic.mathdoc.fr/item/IJAMCS_2003_13_3_a5/