zhang-ahybridapproach-2008.pdf (270.74 kB)
A hybrid approach to selecting susceptible single nucleotide polymorphisms for complex disease analysis
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.
History
Event
IEEE International Conference on BioMedical Engineering and Informatics (1st : 2008 : Hainan, China)Pagination
214 - 218Publisher
IEEELocation
Hainan, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2008-05-27End date
2008-05-30ISBN-13
9780769531182Language
engNotes
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E1 Full written paper - refereedCopyright notice
2008, IEEEEditor/Contributor(s)
Y Peng, Y ZhangTitle of proceedings
BMEI 2008 : Biomedical engineering and informatics : new development and the future : Proceedings of the 1st International Conference on BioMedical Engineering and InformaticsUsage metrics
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