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المؤلفNahas, Laila Dabab
المؤلفDatta, Ankur
المؤلفAlsamman, Alsamman M.
المؤلفAdly, Monica H.
المؤلفAl-Dewik, Nader
المؤلفSekaran, Karthik
المؤلفSasikumar, K.
المؤلفVerma, Kanika
المؤلفDoss, George Priya C.
المؤلفZayed, Hatem
تاريخ الإتاحة2024-03-13T13:38:03Z
تاريخ النشر2024
اسم المنشورMetabolic Brain Disease
المصدرScopus
الرقم المعياري الدولي للكتاب8857490
معرّف المصادر الموحدhttp://dx.doi.org/10.1007/s11011-023-01322-3
معرّف المصادر الموحدhttp://hdl.handle.net/10576/53037
الملخصAutism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by altered brain connectivity and function. In this study, we employed advanced bioinformatics and explainable AI to analyze gene expression associated with ASD, using data from five GEO datasets. Among 351 neurotypical controls and 358 individuals with autism, we identified 3,339 Differentially Expressed Genes (DEGs) with an adjusted p-value (≤ 0.05). A subsequent meta-analysis pinpointed 342 DEGs (adjusted p-value ≤ 0.001), including 19 upregulated and 10 down-regulated genes across all datasets. Shared genes, pathogenic single nucleotide polymorphisms (SNPs), chromosomal positions, and their impact on biological pathways were examined. We identified potential biomarkers (HOXB3, NR2F2, MAPK8IP3, PIGT, SEMA4D, and SSH1) through text mining, meriting further investigation. Additionally, ‎we shed light on the roles of RPS4Y1 and KDM5D genes in neurogenesis and neurodevelopment. Our analysis detected 1,286 SNPs linked to ASD-related conditions, of which 14 high-risk SNPs were located on chromosomes 10 and X. We highlighted potential missense SNPs associated with FGFR inhibitors, suggesting that it may serve as a promising biomarker for responsiveness to targeted therapies. Our explainable AI model identified the MID2 gene as a potential ASD biomarker. This research unveils vital genes and potential biomarkers, providing a foundation for novel gene discovery in complex diseases.
راعي المشروعThe authors would like to take this opportunity to thank the management of Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India, for providing the necessary facilities and encouragement to carry out this work. The authors would also like to acknowledge the efforts of the personnel at Qatar University, Doha, Qatar.
اللغةen
الناشرSpringer
الموضوعArtificial Intelligence' Autism spectrum disorder' Multi-omics' Pathway Enrichment Analysis' SHapley Additive exPlanations' Single nucleotide polymorphism
العنوانGenomic insights and advanced machine learning: characterizing autism spectrum disorder biomarkers and genetic interactions
النوعArticle
الصفحات29-42
رقم العدد1
رقم المجلد39
dc.accessType Open Access


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