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A hybrid approach to selecting susceptible single nucleotide polymorphisms for complex disease analysis

Yang, Pengyi and Zhang, Zili 2008, A hybrid approach to selecting susceptible single nucleotide polymorphisms for complex disease analysis, in BMEI 2008 : Biomedical engineering and informatics : new development and the future : Proceedings of the 1st International Conference on BioMedical Engineering and Informatics, IEEE, Piscataway, N.J., pp. 214-218.

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Title A hybrid approach to selecting susceptible single nucleotide polymorphisms for complex disease analysis
Author(s) Yang, Pengyi
Zhang, Zili
Conference name IEEE International Conference on BioMedical Engineering and Informatics (1st : 2008 : Hainan, China)
Conference location Hainan, China
Conference dates 27-30 May 2008
Title of proceedings BMEI 2008 : Biomedical engineering and informatics : new development and the future : Proceedings of the 1st International Conference on BioMedical Engineering and Informatics
Editor(s) Peng, Yonghong
Zhang, Yufeng
Publication date 2008
Conference series International Conference on BioMedical Engineering and Informatics
Start page 214
End page 218
Publisher IEEE
Place of publication Piscataway, N.J.
Summary An increasingly popular and promising way for complex disease diagnosis is to employ artificial neural networks (ANN). Single nucleotide polymorphisms (SNP) data from individuals is used as the inputs of ANN to find out specific SNP patterns related to certain disease. Due to the large number of SNPs, it is crucial to select optimal SNP subset and their combinations so that the inputs of ANN can be reduced. With this observation in mind, a hybrid approach - a combination of genetic algorithms (GA) and ANN (called GANN) is used to automatically determine optimal SNP set and optimize the structure of ANN. The proposed GANN algorithm is evaluated by using both a synthetic dataset and a real SNP dataset of a complex disease.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 9780769531182
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30018221

Document type: Conference Paper
Collections: School of Information Technology
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