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Journal cover: COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

ISSN: 0332-1649

Online from: 1982

Subject Area: Electrical & Electronic Engineering

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Gene selection for cancer classification


Document Information:
Title:Gene selection for cancer classification
Author(s):Artur Wilinski, (Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland), Stanislaw Osowski, (Institute of the Theory of Electrical Engineering, Measurement and Information Systems, Warsaw University of Technology, Warsaw, Poland Institute of Electronic Systems, Military University of Technology, Warsaw, Poland)
Citation:Artur Wilinski, Stanislaw Osowski, (2009) "Gene selection for cancer classification", COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 28 Iss: 1, pp.231 - 241
Keywords:Cancer, Classification, Genes
Article type:Research paper
DOI:10.1108/03321640910919020 (Permanent URL)
Publisher:Emerald Group Publishing Limited
Abstract:

Purpose – The purpose of this paper is to discover the most important genes generated by the gene expression arrays, responsible for the recognition of particular types of cancer.

Design/methodology/approach – The paper presents the analysis of different techniques of gene selection, including correlation, statistical hypothesis, clusterization and linear support vector machine (SVM).

Findings – The correctness of the gene selection is proved by mapping the distribution of selected genes on the two-coordinate system formed by two most important principal components of the PCA transformation. Final confirmation of this approach are the classification results of recognition of several types of cancer, performed using Gaussian kernel SVM.

Originality/value – The results of selection of the most significant genes used for the SVM recognition of seven types of cancer have confirmed good accuracy of results. The presented methodology is of potential use in practical application in bioinformatics.



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