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      DeepND: Deep multitask learning of gene risk for comorbid neurodevelopmental disorders

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      Author(s)
      Beyreli, İlayda
      Karakahya, Oğuzhan
      Çiçek, A. Ercüment
      Date
      2022-07-08
      Source Title
      Patterns
      Print ISSN
      26663899
      Publisher
      Cell Press
      Volume
      3
      Issue
      7
      Pages
      1 - 16
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      Autism spectrum disorder and intellectual disability are comorbid neurodevelopmental disorders with complex genetic architectures. Despite large-scale sequencing studies, only a fraction of the risk genes was identified for both. We present a network-based gene risk prioritization algorithm, DeepND, that performs cross-disorder analysis to improve prediction by exploiting the comorbidity of autism spectrum disorder (ASD) and intellectual disability (ID) via multitask learning. Our model leverages information from human brain gene co-expression networks using graph convolutional networks, learning which spatiotemporal neurodevelopmental windows are important for disorder etiologies and improving the state-of-the-art prediction in single- and cross-disorder settings. DeepND identifies the prefrontal and motor-somatosensory cortex (PFC-MFC) brain region and periods from early- to mid-fetal and from early childhood to young adulthood as the highest neurodevelopmental risk windows for ASD and ID. We investigate ASD- and ID-associated copy-number variation (CNV) regions and report our findings for several susceptibility gene candidates. DeepND can be generalized to analyze any combinations of comorbid disorders. © 2022 The Author(s)
      Keywords
      Autism
      Comorbidity
      Deep learning
      Development/pre-production
      DSML3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems
      Genome-wide association
      Graph convolution
      Intellectual disability
      Node classification
      Semisupervised learning
      Permalink
      http://hdl.handle.net/11693/111946
      Published Version (Please cite this version)
      https://dx.doi.org/10.1016/j.patter.2022.100524
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