title: A novel SVM-ID3 hybrid feature selection method to build a disease model for melanoma using integrated genotyping and phenotype data from dbGaP.
abstract: The relations between Single Nucleotide Polymorphism (SNP) and complex diseases are likely to be non-linear and require analysis of the high dimensional data. Previous studies in the field mostly focus on genotyping and effects of various phenotypes are not considered. To fill this gap a hybrid feature selection model of support vector machine and decision tree has been designed. The designed method is tested on melanoma. We were able to select phenotypic features such as moles and dysplastic nevi, and SNPs those maps to specific genes such as CAMK1D. The performance results of the proposed hybrid model, on melanoma dataset are 79.07% of sensitivity and 0.81 of area under ROC curve.
authors.affiliation: Medical Informatics Department & Bioinformatics Graduate Program. Graduate School of Informatics, Middle East Technical University. Ankara, Turkey.
authors.forename: Yeşim Aydın
authors.initials: YA
authors.lastname: Son
authors.affiliation: Department of Computer Engineering, Faculty of Engineering, Çanakkale Onsekiz Mart University. Çanakkale, Turkey.
authors.forename: Sait Can
authors.initials: SC
authors.lastname: Yücebaş
mesh_terms: D003198:Computer Simulation; D030541:Databases, Genetic; D020000:Decision Support Systems, Clinical; D005060:Europe; D020022:Genetic Predisposition to Disease / Q000453:epidemiology / Q000235:genetics; D055106:Genome-Wide Association Study / Q000379:methods; D005838:Genotype; D006801:Humans; D008545:Melanoma / Q000175:diagnosis* / Q000453:epidemiology / Q000235:genetics*; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000175:diagnosis* / Q000453:epidemiology / Q000235:genetics*; D060388:Support Vector Machine; D018511:Systems Integration