A Hybrid Feature Selection Model For Genome Wide Association Studies - Yucebas, Sait Can
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Through Genome Wide Association Studies (GWAS) many SNP-complex disease relations have been investigated so far. GWAS presents high amount - high dimensional data and relations between SNPs, phenotypes and diseases are most likely to be nonlinear. In order to handle high volume-high dimensional data and to be able to find the nonlinear relations, data mining approaches are needed. In this work, a hybrid feature selection model of support vector machine and decision tree has been designed. This model also combines the genotype and phenotype information to increase the diagnostic performance.…mehr

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Produktbeschreibung
Through Genome Wide Association Studies (GWAS) many SNP-complex disease relations have been investigated so far. GWAS presents high amount - high dimensional data and relations between SNPs, phenotypes and diseases are most likely to be nonlinear. In order to handle high volume-high dimensional data and to be able to find the nonlinear relations, data mining approaches are needed. In this work, a hybrid feature selection model of support vector machine and decision tree has been designed. This model also combines the genotype and phenotype information to increase the diagnostic performance. The model is tested on prostate cancer and melanoma data and shows promising results.
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Autorenporträt
Dr. Sait Can YUCEBAS is a computer engineer holding a M.Sc. in Computer Engineering and a Ph.D. in Medical Informatics. In his Ph.D. study, he focused on modeling of complex diseases based on integrated genomic and clinical data. His field of interests are artificial intelligence, datamining, tele-health,mobile-health, bioinformatics.