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| http://dx.doi.org/10.1002/mds.29765 | DOI Listing |
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Systemic study of pathogenic pathways and interrelationships underlying genes associated with Alzheimer's disease (AD) facilitates the identification of new targets for effective treatments. Recently available large-scale multiomics datasets provide opportunities to use computational approaches for such studies. Here, we devised a novel sease ene entification (digID) computational framework that consists of a semi-supervised deep learning classifier to predict AD-associated genes and a protein-protein interaction (PPI) network-based analysis to prioritize the importance of these predicted genes in AD.
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School of Life Sciences, Shanghai University, 200444 Shanghai, China.
Background: Autophagy is instrumental in various health conditions, including cancer, aging, and infections. Therefore, examining proteins and compounds associated with autophagy is paramount to understanding cellular biology and the origins of diseases, paving the way for potential therapeutic and disease prediction strategies. However, the complexity of autophagy, its intersection with other cellular pathways, and the challenges in monitoring autophagic activity make the experimental identification of these elements arduous.
View Article and Find Full Text PDFNovel de novo pathogenic variant in the GNAI1 gene as a cause of severe disorders of intellectual development.
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Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, Czech Republic.
Pathogenic sequence variant in the GNAI1 gene were recently introduced as a cause of novel syndrome with a manifestation of variable developmental delay and autistic features. In our study, we report a case of monozygotic twins with severe intellectual disability and motor delay and developmental dysphasia. Both probands and their parents were examined using multi-step molecular diagnostic algorithm including whole-exome sequencing (WES), resulting in the identification of a novel, de novo pathogenic sequence variant in the GNAI1 gene, NM_002069.
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