Symptom based classification of copy number variations underlying the multidimensional autism spectrum disorder phenotype using machine learning methods
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info:eu-repo/semantics/openAccess
Özet
Purpose 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.
Açıklama
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CNVs, Machine learning, Neurodevelopmental disorders, CNV'ler, Makine öğrenimi, Nörogelişimsel bozukluklar
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58th Conference of the European-Society-of-Human-Genetics (ESHG)
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Bolat, 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.
