Symptom based classification of copy number variations underlying the multidimensional autism spectrum disorder phenotype using machine learning methods

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dc.contributor.authorBolat, Hilmi
dc.contributor.authorBolat, Gül Ünsel
dc.contributor.authorBulut, Edanur
dc.contributor.authorKaya, Özge Özer
dc.contributor.authorGüvenç, Merve Saka
dc.contributor.authorArıkan, Şener
dc.contributor.authorKırbıyık, Özgür
dc.contributor.authorTürk, Tuba Sözen
dc.contributor.authorKoç, Altuğ
dc.contributor.authorÖzdemir, Taha Reşid
dc.contributor.authorKutbay, Yaşar
dc.contributor.authorErdoğan, Kadri Murat
dc.contributor.authorÖzgül, Semiha
dc.contributor.authorTuran, Duygu Selin
dc.contributor.authorÇelik, Samet
dc.contributor.authorKoyuncu, Özgur Ozan
dc.contributor.authorEngin, Gonca
dc.contributor.authorOrdin, Burak
dc.contributor.authorÖzyılmaz, Berk
dc.contributor.authorKosova, Buket
dc.contributor.authorÇelik, Samet
dc.contributor.otherİnsan ve Toplum Bilimleri Fakültesi, Psikoloji Bölümü
dc.date.accessioned2026-04-09T07:39:59Z
dc.date.created2025
dc.date.issued2025
dc.departmentFakülteler, Edebiyat Fakültesi, Psikoloji Bölümü
dc.description.abstractPurpose Copy number variations (CNVs) in the region are well-established contributors to neurodevelopmental disorders, yet phenotype variability across this locus remains insufficiently characterized. This study investigates clinical features and ASD-related symptoms among carriers of rare pathogenic and common CNVs, and evaluates symptom-level discriminability using machine learning (ML) methods.
dc.identifier.citationBolat, H., Bolat, G. Ü., Bulut, E., Kaya, Ö. Ö., Güvenç, M. S., Arikan, S., Kirbiyik, Ö., Türk, T. S., Koç, A., Özdemir, T. R., Kutbay, Y., Erdogan, K. M., Özgül, S., Turan, D. S., Çelik, S., Koyuncu, Ö. O., Engin, G., Ordin, B., Özyilmaz, B., & Kosova, B. (2025, May 24–27). Symptom based classification of copy number variations underlying the multidimensional autism spectrum disorder phenotype using machine learning methods [Meeting abstract, P17.147.D]. 58th Conference of the European Society of Human Genetics (ESHG), Milan, Italy.
dc.identifier.endpage1019
dc.identifier.startpage1018
dc.identifier.urihttps://hdl.handle.net/11772/27080
dc.identifier.wosWOS:001671157905320
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.relation.ispartof58th Conference of the European-Society-of-Human-Genetics (ESHG)
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCNVs
dc.subjectMachine learning
dc.subjectNeurodevelopmental disorders
dc.subjectCNV'ler
dc.subjectMakine öğrenimi
dc.subjectNörogelişimsel bozukluklar
dc.titleSymptom based classification of copy number variations underlying the multidimensional autism spectrum disorder phenotype using machine learning methods
dc.typeConference Object
dspace.entity.typePublication
relation.isAuthorOfPublicationd4df094a-9dd6-4ea0-a858-3354d992fb5a
relation.isAuthorOfPublication.latestForDiscoveryd4df094a-9dd6-4ea0-a858-3354d992fb5a
relation.isOrgUnitOfPublication509dbd27-3166-48be-b100-dd937a3ae032
relation.isOrgUnitOfPublication.latestForDiscovery509dbd27-3166-48be-b100-dd937a3ae032

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