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.
(Some files may be inaccessible until you login with your Deakin Research Online credentials)
BMEI 2008 : Biomedical engineering and informatics : new development and the future : Proceedings of the 1st International Conference on BioMedical Engineering and Informatics
Peng, Yonghong Zhang, Yufeng
International Conference on BioMedical Engineering and Informatics
Place of publication
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.
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.
Field of Research
080199 Artificial Intelligence and Image Processing not elsewhere classified
Unless expressly stated otherwise, the copyright for items in Deakin Research Online is owned by the author, with all rights reserved.
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO.
If you believe that your rights have been infringed by this repository, please contact email@example.com.