DeepND: Deep multitask learning of gene risk for comorbid neurodevelopmental disorders
| Autor: | Ilayda Beyreli, Oguzhan Karakahya, A. Ercument Cicek |
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| Přispěvatelé: | Beyreli, İlayda, Karakahya, Oğuzhan, Çiçek, A. Ercüment |
| Rok vydání: | 2022 |
| Předmět: |
Graph convolution
Genome-wide association Node classification Autism Intellectual disability General Decision Sciences Deep learning Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems [DSML3] Semisupervised learning Comorbidity Development/pre-production |
| Zdroj: | Patterns |
| ISSN: | 2666-3899 |
| Popis: | 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) |
| Databáze: | OpenAIRE |
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