title   
  

Evolutionary selection of minimum number of features for classification of gene expression data using genetic algorithms

Küçükural, Alper and Yeniterzi, Reyyan and Yeniterzi, Süveyda and Sezerman, Uğur (2007) Evolutionary selection of minimum number of features for classification of gene expression data using genetic algorithms. In: 9th Annual Conference on Genetic and Evolutionary Computation (GECCO 2007), London, England

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Official URL: http://doi.acm.org/10.1145/1276958.1277040

Abstract

Selecting the most relevant factors from genetic profiles that can optimally characterize cellular states is of crucial importance in identifying complex disease genes and biomarkers for disease diagnosis and assessing drug efficiency. In this paper, we present an approach using a genetic algorithm for a feature subset selection problem that can be used in selecting the near optimum set of genes for classification of cancer data. In substantial improvement over existing methods, we classified cancer data with high accuracy with less features.

Item Type:Papers in Conference Proceedings
Additional Information:Genetic And Evolutionary Computation Conference: Proceedings of the 9th annual conference on Genetic and evolutionary computation: London, England: SESSION: Biological applications
Uncontrolled Keywords:biomarkers, classification, colon cancer, feature selection, genetic algorithms, ovarian cancer, prostate cancer
Subjects:Q Science > Q Science (General)
ID Code:6948
Deposited By:Uğur Sezerman
Deposited On:03 Nov 2007 15:47
Last Modified:25 May 2011 14:19

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