Creator:
Fujarewicz, Krzysztof ; Wiench, Małgorzata
Contributor:
Kimmel, Marek - red. ; Lachowicz, Mirosław - red. ; Świerniak, Andrzej - red.
Title:
Selecting differentially expressed genes for colon tumor classification
Subtitle:
Cancer Growth and Progression, Mathematical Problems and Computer Simulations
Group publication title:
Subject and Keywords:
colon tumor ; gene expression data ; microarrays ; support vector machines ; feature selection ; classification
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 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.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Date:
Resource Type:
Pages:
Source:
AMCS, volume 13, number 3 (2003) ; click here to follow the link