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dc.contributor.advisorÇiçek, A. Ercüment
dc.contributor.authorNorman, Utku
dc.date.accessioned2018-08-10T13:46:37Z
dc.date.available2018-08-10T13:46:37Z
dc.date.copyright2018-05
dc.date.issued2018-08
dc.date.submitted2018-08-09
dc.identifier.urihttp://hdl.handle.net/11693/47739
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2018.en_US
dc.descriptionIncludes bibliographical references (leaves 32-39).en_US
dc.description.abstractWhole Exome Sequencing (WES) studies for Autism Spectrum Disorder (ASD) could identify only around six dozen risk genes to date, because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited. Here, we present a spatiotemporal gene discovery algorithm for ASD, which leverages information from evolving gene coexpression networks of neurodevelopment. The algorithm solves a prize-collecting Steiner forest based problem on coexpression networks, adapted to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on WES data of 3,871 samples and identify risk clusters using BrainSpan coexpression networks of early- and mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the predictive power: Predicted clusters are hit more (i.e. they contain genes with more disruptive mutations on them) and show higher enrichment in ASD-related functions compared to the state of the art.en_US
dc.description.statementofresponsibilityby Utku Normanen_US
dc.format.extentxi, 58 leaves : charts (some color) ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSpatio-temporal networksen_US
dc.subjectGene discoveryen_US
dc.subjectPrize-collecting Steiner forest problemen_US
dc.subjectAutism spectrum disorderen_US
dc.titleSpatio-temporal gene discovery for autism spectrum disorderen_US
dc.title.alternativeOtizm spektrum bozukluğu için zaman-mekansal gen keşfien_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB158765


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