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dc.contributor.authorGormez, Yasin
dc.contributor.authorIsik, Yunus Emre
dc.contributor.authorBakir-Gungor, Burcu
dc.date.accessioned2021-05-24T11:39:06Z
dc.date.available2021-05-24T11:39:06Z
dc.date.issued2018en_US
dc.identifier.isbn978-1-5386-7893-0
dc.identifier.urihttps://hdl.handle.net/20.500.12573/747
dc.description.abstractBehcet's disease is a long-term multisystem inflammatory disorder, characterized by recurrent attacks affecting several organs. As the genotyping individuals get cheaper and easier following the developments in genomic technologies, genome-wide association studies (GWAS) emerged. By this means, via studying big-sized case-control groups for a specific disease, potential genetic variations, single nucleotide polymorphisms (SNPs) are identified. Although several genetic risk factors are identified for Behcet's disease with the help of these studies via scanning around a million of SNPs, these variations could only explain up to 200/u of the disease's genetic risk. In this study, for Behcet's disease classification, via comparing all the SNPs genotyped in GWAS, with the SNPs selected via using genetic knowledge, gain ratio and information gain; both reduction in the feature size and improvement in the classification accuracy is aimed. Also, using different classification algorithms such as random forest, k-nearest neighbour and logistic regression, their effects on the classification accuracy are investigated. Our results showed that compared to other feature selection methods, with at least 81% success rate, the selection of the SNPs using the genetic information (of their GWAS p-values, indicating the significance of the SNP against the disease) provides 15% to 42% improvement in all classification algorithms. This improvement is statistically sound. While gain ratio and information gain feature selection techniques yield similar classification accuracies, the models using all SNPs could not exceed 50% accuracies and results in the worst performance.en_US
dc.description.sponsorshipBMBB; Istanbul Teknik Univ; Gazi Univ; ATILIM Univ; Int Univ Sarajevo; Kocaeli Univ; TURKiYE BiLiSiM VAKFIen_US
dc.language.isoturen_US
dc.publisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USAen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectgenome-wide association study (GWAS)en_US
dc.subjectsingle nucleotide polymorphism (SNP)en_US
dc.subjectfeature selectionen_US
dc.subjectmachine learningen_US
dc.subjectBehcet's diseaseen_US
dc.titleThe Identification of Discriminative Single Nucleotide Polymorphism Sets for the Classification of Behcet's Diseaseen_US
dc.typeconferenceObjecten_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.contributor.authorID0000-0002-2272-6270en_US
dc.relation.journal2018 3RD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK)en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - Kurum Öğretim Elemanıen_US


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