dc.contributor.advisor | Çiçek, A. Ercüment | |
dc.contributor.author | Karakahya, Oğuzhan | |
dc.date.accessioned | 2020-11-18T06:22:22Z | |
dc.date.available | 2020-11-18T06:22:22Z | |
dc.date.copyright | 2020-10 | |
dc.date.issued | 2020-10 | |
dc.date.submitted | 2020-11-17 | |
dc.identifier.uri | http://hdl.handle.net/11693/54523 | |
dc.description | Cataloged from PDF version of article. | en_US |
dc.description | Thesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2020. | en_US |
dc.description | Includes bibliographical references (leaves 47-59). | en_US |
dc.description.abstract | Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder
with a strong genetic basis. Due to its intricate nature, only a fraction of the risk
genes were identified despite the effort spent on large-scale sequencing studies.
To perceive underlying mechanisms of ASD and predict new risk genes, a deep
learning architecture is designed which processes mutational burden of genes and
gene co-expression networks using graph convolutional networks. In addition,
a mixture of experts model is employed to detect specific neurodevelopmental
periods that are of particular importance for the etiology of the disorder. This
end-to-end trainable model produces a posterior ASD risk probability for each
gene and learns the importance of each network for this prediction. The results of
our approach show that the ASD gene risk prediction power is improved compared
to the state-of-the-art models. We identify mediodorsal nucleus of thalamus and
cerebellum brain region and neonatal & early infancy to middle & late childhood
period (0 month - 12 years) as the most informative neurodevelopmental window
for prediction. Top predicted risk genes are found to be highly enriched in ASDassociated pathways and transcription factor targets. We pinpoint several new candidate risk genes in CNV regions associated with ASD. We also investigate
confident false-positives and false negatives of the method and point to studies
which support the predictions of our method. | en_US |
dc.description.statementofresponsibility | by Oğuzhan Karakahya | en_US |
dc.format.extent | xv, 81 leaves : charts ; 30 cm. | en_US |
dc.language.iso | English | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Autism spectrum disorder | en_US |
dc.subject | Graph convolutional networks | en_US |
dc.subject | Deep learning | en_US |
dc.title | Predicting informative spatio-temporal neurodevelopmental windows and gene risk for autism spectrum disorder | en_US |
dc.title.alternative | Otizm spektrum bozukluğu için bilgi verici zaman-uzamsal sinir gelişim aralığı ve gen riski tahmini | en_US |
dc.type | Thesis | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.publisher | Bilkent University | en_US |
dc.description.degree | M.S. | en_US |
dc.identifier.itemid | B149099 | |