Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17988
Title: Gene expression analyses using genetic algorithm based hybrid approaches
Authors: Chen, D
Chan, KCC 
Wu, X
Keywords: Genetic algorithms
Genetics
Medical diagnostic computing
Support vector machines
Tumours
Issue Date: 2008
Publisher: IEEE
Source: Evolutionary Computation, 2008, CEC 2008, (IEEE World Congress on Computational Intelligence), IEEE Congress on : date, 1-6 June, 2008, Hong Kong, p. 963-969 How to cite?
Abstract: This paper presents two genetic algorithm (GA) based hybrid approaches for the prediction of tumor outcomes based on gene expression data. One approach is the hybrid GA and K-medoids for grouping genes based on the commonly used distance similarity. The goal of grouping genes here is to choose some top-ranked representatives from each cluster for gene dimensionality reduction. The second proposed approach is the hybrid GA and Support Vector Machines (SVM) for selecting marker genes and classifying tumor types or predicting treatment outcomes. These two hybrid approaches have been applied to public brain cancer datasets, and the experimental results are compared with those given in a 2001 paper published in the Nature. The final prediction accuracies are found to be superior both for tumor class prediction and treatment outcome prediction.
URI: http://hdl.handle.net/10397/17988
ISBN: 978-1-4244-1822-0
978-1-4244-1823-7 (E-ISBN)
DOI: 10.1109/CEC.2008.4630913
Appears in Collections:Conference Paper

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