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