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      •   BUIR Home
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      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
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      Multitask learning of gene risk for autism spectrum disorder and intellectual disability

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      Author(s)
      Beyreli, İlayda
      Advisor
      Çiçek, Abdullah Ercüment
      Date
      2020-11
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      Abstract
      Autism Spectrum Disorder (ASD) and Intellectual Disability (ID) are comorbid neurodevelopmental disorders with complex genetic architectures. Despite largescale sequencing studies only a fraction of the risk genes were identified for both. Here, we present a novel network-based gene risk prioritization algorithm named DeepND that performs cross-disorder analysis to improve prediction power by exploiting the comorbidity of ASD and ID via multitask learning. Our model leverages information from gene co-expression networks that model human brain development using graph convolutional neural networks and learns which spatiotemporal neurodevelopmental windows are important for disorder etiologies. We show that our approach substantially improves the state-of-the-art prediction power. We observe that both disorders are enriched in transcription regulators. Despite tight regulatory links in between ASD risk genes, such is lacking across ASD and ID risk genes or within ID risk genes. Finally, we investigate frequent ASD and ID associated copy number variation regions and confident false findings to suggest several novel susceptibility gene candidates. DeepND can be generalized to analyze any combinations of comorbid disorders.
      Keywords
      Autism spectrum disorder
      Intellectual disability
      Comorbidity
      Multitask learning
      Graph convolutional networks
      Deep learning
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      http://hdl.handle.net/11693/54524
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      • Dept. of Computer Engineering - Master's degree 517
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