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10.1007_s11011-023-01322-3.pdf (2.24 MB)

Genomic insights and advanced machine learning: characterizing autism spectrum disorder biomarkers and genetic interactions

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submitted on 2024-01-14, 06:31 and posted on 2024-01-16, 04:51 authored by Laila Dabab Nahas, Ankur Datta, Alsamman M. Alsamman, Monica H. Adly, Nader Al-Dewik, Karthik Sekaran, K Sasikumar, Kanika Verma, George Priya C Doss, Hatem Zayed

Autism 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.

Other Information

Published in: Metabolic Brain Disease
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Open Access funding provided by the Qatar National Library.



  • English


Springer Nature

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Qatar University
  • Qatar University Health - QU
  • College of Health Sciences - QU HEALTH
  • Hamad Medical Corporation
  • Women's Wellness and Research Center - HMC