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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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.
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Field of Research
080199 Artificial Intelligence and Image Processing not elsewhere classified
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