Hybrid methods to select informative gene sets in microarray data classification

Yang, Pengyi and Zhang, Zili 2007, Hybrid methods to select informative gene sets in microarray data classification, Lecture Notes in Computer Science, vol. 4830, pp. 810-814.

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Title Hybrid methods to select informative gene sets in microarray data classification
Author(s) Yang, Pengyi
Zhang, ZiliORCID iD for Zhang, Zili orcid.org/0000-0002-8721-9333
Journal name Lecture Notes in Computer Science
Volume number 4830
Start page 810
End page 814
Publisher Springer Verlag
Place of publication Berlin, Germany
Publication date 2007
ISSN 0302-9743
Summary 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.
Notes Book title: AI 2007: Advances in artificial intelligence
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2007, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30007420

Document type: Journal Article
Collection: School of Engineering and Information Technology
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