SynthesisMetabolic brain disease2024
Genomic insights and advanced machine learning: characterizing autism spectrum disorder biomarkers and genetic interactions.
Synthesis in Metabolic brain disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Unraveling the Role of Wnt Signaling Pathway in the Pathogenesis of Autism Spectrum Disorder (ASD): A Systematic Review.Molecular neurobiology · 2025Pooled it
- A Critical Synthesis of Machine Learning in Autism Spectrum Disorder Genomic Research: From Transcriptomics to Microbiome.Medeniyet medical journal · 2026Article
- Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.Molecular brain · 2026Review
- Early autism detection: a review of emerging technologies, biomarkers, and explainable AI approaches.Molecular brain · 2025Review
- AI-Enabled Technologies and Biomarker Analysis for the Early Identification of Autism and Related Neurodevelopmental Disorders.Children (Basel, Switzerland) · 2025Review
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- Association between GRIK1 rs363598 and intergenic rs360932 variants and susceptibility to autism spectrum disorders in Egyptian children.BMC pediatrics · 2025Article
- An overview of oxytocin integrative mechanisms in autism spectrum disorder.Discover mental health · 2025Review
- ZNRF3 in neurodevelopmental disorders: insights into Wnt signaling and therapeutic potential.Neurogenetics · 2025Review
- Genomic and Structural Investigation of Mutations in Biotinidase (BTD) Gene Deficiency in Greater Middle Eastern Cohort: Insights from Molecular Dynamics Study.Biomedicines · 2025Article
- Bind: large-scale biological interaction network discovery through knowledge graph-driven machine learning.Journal of translational medicine · 2025Article
- Neurocognitive mechanisms underlying autism spectrum disorders: a literature review.Frontiers in psychiatry · 2025Review
- Using explainable machine learning and eye-tracking for diagnosing autism spectrum and developmental language disorders in social attention tasks.Frontiers in neuroscience · 2025Article
- Biomarkers for autism spectrum disorder: a short review.Annals of medicine and surgery (2012) · 2024Review
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Proteomics analysis of extracellular vesicles for biomarkers of autism spectrum disorder.Frontiers in molecular biosciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.