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Hybrid methods to select informative gene sets in microarray data classification

journal contribution
posted on 2007-01-01, 00:00 authored by P Yang, Zili ZhangZili Zhang
One of the key applications of microarray studies is to select and classify gene expression profiles of cancer and normal subjects. In this study, two hybrid approaches–genetic algorithm with decision tree (GADT) and genetic algorithm with neural network (GANN)–are utilized to select optimal gene sets which contribute to the highest classification accuracy. Two benchmark microarray datasets were tested, and the most significant disease related genes have been identified. Furthermore, the selected gene sets achieved comparably high sample classification accuracy (96.79% and 94.92% in colon cancer dataset, 98.67% and 98.05% in leukemia dataset) compared with those obtained by mRMR algorithm. The study results indicate that these two hybrid methods are able to select disease related genes and improve classification accuracy.

History

Journal

Lecture Notes in Computer Science

Volume

4830

Pagination

810 - 814

Publisher

Springer Verlag

Location

Berlin, Germany

ISSN

0302-9743

eISSN

1611-3349

Language

eng

Notes

Book title: AI 2007: Advances in artificial intelligence

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2007, Springer-Verlag Berlin Heidelberg

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