The Determination of Distinctive Single Nucleotide Polymorphism Sets for the Diagnosis of Behcet's Disease

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dc.authoridgormez, yasin/0000-0001-8276-2030
dc.authoridIsik, Yunus Emre/0000-0001-6176-7545
dc.contributor.authorIsik, Yunus Emre
dc.contributor.authorGormez, Yasin
dc.contributor.authorAydin, Zafer
dc.contributor.authorBakir-Gungor, Burcu
dc.date.accessioned2024-10-26T18:05:39Z
dc.date.available2024-10-26T18:05:39Z
dc.date.issued2022
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractBehcet's Disease (BD) is a multi-system inflammatory disorder in which the etiology remains unclear. The most probable hypothesis is that genetic tendency and environmental factors play roles in the development of BD. In order to find the essential reasons, genetic changes on thousands of genes should be analyzed. Besides, there is a need for extra analysis to find out which genetic factor affects the disease. Machine learning approaches have high potential for extracting the knowledge from genomics and selecting the representative Single Nucleotide Polymorphisms (SNPs) as the most effective features for the clinical diagnosis process. In this study, we have attempted to identify representative SNPs using feature selection methods, incorporating biological information and aimed to develop a machine-learning model for diagnosing Behcet's disease. By combining biological information and machine learning classifiers, up to 99.64 percent accuracy of disease prediction is achieved using only 13,611 out of 311,459 SNPs. In addition, we revealed the SNPs that are most distinctive by performing repeated feature selection in cross-validation experiments.
dc.description.sponsorshipAbdullah Gul University Support Foundation (AGUV)
dc.description.sponsorshipThe numerical calculations reported in this paper were partially performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources). We would like to thank Prof. Ahmet Gul and his team members for sharing the dataset with us. We would like to thank Prof. Ahmet Gul and his team members for sharing the dataset with us. The work of Burcu Bakir-Gungor was supported by the Abdullah Gul University Support Foundation (AGUV).
dc.identifier.doi10.1109/TCBB.2021.3053429
dc.identifier.endpage1918
dc.identifier.issn1545-5963
dc.identifier.issn1557-9964
dc.identifier.issue3
dc.identifier.pmid33476272
dc.identifier.scopus2-s2.0-85100465018
dc.identifier.scopusqualityQ2
dc.identifier.startpage1909
dc.identifier.urihttps://doi.org/10.1109/TCBB.2021.3053429
dc.identifier.urihttps://hdl.handle.net/20.500.12418/29118
dc.identifier.volume19
dc.identifier.wosWOS:000805807200063
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherIEEE Computer Soc
dc.relation.ispartofIeee-Acm Transactions on Computational Biology and Bioinformatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDiseases
dc.subjectFeature extraction
dc.subjectMachine learning
dc.subjectPredictive models
dc.subjectBioinformatics
dc.subjectSupport vector machines
dc.subjectRadio frequency
dc.subjectBehcet's disease (BD)
dc.subjectfeature selection
dc.subjectmachine learning
dc.subjectdisease prediction
dc.subjectmost informative SNPs
dc.titleThe Determination of Distinctive Single Nucleotide Polymorphism Sets for the Diagnosis of Behcet's Disease
dc.typeArticle

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