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A novel SVM-ID3 Hybrid Feature Selection Method to Build a Disease Model for Melanoma using Integrated Genotyping and Phenotype Data from dbGaP
Date
2014-09-03
Author
Aydın Son, Yeşim
YÜCEBAŞ, SAİT CAN
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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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.
Subject Keywords
Genome Wide Association Studies (GWAS)
,
Single Nucleotide Polymorphism (SNP)
,
Data mining
,
Support Vector Machines (SVM)
,
Decision trees
,
Hybrid models
URI
https://hdl.handle.net/11511/31532
DOI
https://doi.org/10.3233/978-1-61499-432-9-501
Collections
Graduate School of Informatics, Conference / Seminar