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

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buir.contributor.authorBeyreli, İlayda
buir.contributor.authorKarakahya, Oğuzhan
buir.contributor.authorÇiçek, A. Ercüment
buir.contributor.orcidBeyreli, İlayda|0000-0003-0305-1965
buir.contributor.orcidKarakahya, Oğuzhan|0000-0001-6837-4007
buir.contributor.orcidÇiçek, A. Ercüment|0000-0001-8613-6619
dc.citation.epage16en_US
dc.citation.issueNumber7en_US
dc.citation.spage1en_US
dc.citation.volumeNumber3en_US
dc.contributor.authorBeyreli, İlayda
dc.contributor.authorKarakahya, Oğuzhan
dc.contributor.authorÇiçek, A. Ercüment
dc.date.accessioned2023-02-28T13:25:57Z
dc.date.available2023-02-28T13:25:57Z
dc.date.issued2022-07-08
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractAutism 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)en_US
dc.identifier.doi10.1016/j.patter.2022.100524en_US
dc.identifier.issn26663899
dc.identifier.urihttp://hdl.handle.net/11693/111946
dc.language.isoEnglishen_US
dc.publisherCell Pressen_US
dc.relation.isversionofhttps://dx.doi.org/10.1016/j.patter.2022.100524en_US
dc.source.titlePatternsen_US
dc.subjectAutismen_US
dc.subjectComorbidityen_US
dc.subjectDeep learningen_US
dc.subjectDevelopment/pre-productionen_US
dc.subjectDSML3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problemsen_US
dc.subjectGenome-wide associationen_US
dc.subjectGraph convolutionen_US
dc.subjectIntellectual disabilityen_US
dc.subjectNode classificationen_US
dc.subjectSemisupervised learningen_US
dc.titleDeepND: Deep multitask learning of gene risk for comorbid neurodevelopmental disordersen_US
dc.typeArticleen_US
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