Utilising Neural Network and Support Vector Machine for Gene Expression Classification

Keivan Kianmehr (), Hongchao Zhang (), Konstantin Nikolov (), Tansel Özyer () and Reda Alhajj ()
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Keivan Kianmehr: Department of Computer Science, University of Calgary Calgary, Alberta, Canada
Hongchao Zhang: Department of Computer Science, University of Calgary Calgary, Alberta, Canada
Konstantin Nikolov: Department of Computer Science, University of Calgary Calgary, Alberta, Canada
Tansel Özyer: Department of Computer Science, University of Calgary Calgary, Alberta, Canada
Reda Alhajj: Department of Computer Science, University of Calgary Calgary, Alberta, Canada

Journal of Information & Knowledge Management (JIKM), 2007, vol. 06, issue 04, 251-260

Abstract: Bioinformatics is the science of managing, mining and interpreting information from biological sequences and structures. In this paper, we discuss two data-mining techniques that can be applied in bioinformatics: Neural Networks (NN) and Support Vector Machines (SVMs), and their application in gene expression classification. First, we provide a description of the two techniques. Then, we propose a new method that combines both SVM and NN. This way, we provide an effective knowledge management technique by utilising machine-learning techniques within the data-mining process. The knowledge obtained from the process is valuable as it is not possible to discover the same kind of knowledge using classical query processing or knowledge management techniques. Finally, we present the results obtained from our method and the results obtained from SVM alone on a sample data set.

Keywords: Bioinformatics; data mining; knowledge discovery; feature selection; neural networks; support vector machines (search for similar items in EconPapers)
Date: 2007
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219649207001822

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