DocumentCode :
3399464
Title :
A genetic algorithm applied to optimal gene subset selection
Author :
Ding, ShengChao ; Liu, Juan ; Wu, ChahLe ; Yang, Qing
Author_Institution :
Sch. of Comput. Sci., Wuhan Univ., Hubei, China
Volume :
2
fYear :
2004
fDate :
19-23 June 2004
Firstpage :
1654
Abstract :
Optimal gene subset selection plays an important role in classification of patient samples. Different to existed methods, we propose a novel optimal gene subset selection approach based on genetic algorithms (GAs). Special fitness function is applied in this scheme. Going beyond other methods, this GA-based method automatically determines the members of a predictive gene group, as well as the optimal group size. The evaluation experiments are applied to two data sets. The results and some discussions are presented too.
Keywords :
biomedical imaging; genetic algorithms; image classification; learning (artificial intelligence); medical diagnostic computing; tumours; data sets; genetic algorithm; optimal gene subset selection; optimal group size; patient samples; predictive gene group; special fitness function; Cancer; Computer science; Content addressable storage; DNA; Gene expression; Genetic algorithms; Medical treatment; Microscopy; Monitoring; Neoplasms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2004. CEC2004. Congress on
Print_ISBN :
0-7803-8515-2
Type :
conf
DOI :
10.1109/CEC.2004.1331094
Filename :
1331094
Link To Document :
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