ArticleScientific reports2020
Forecasting risk gene discovery in autism with machine learning and genome-scale data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers.
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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.
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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.
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Who cites it
26 citing papers in PubMed, 51 citations in OpenAlex.
- Safety and target engagement of an oral small-molecule sequestrant in adolescents with autism spectrum disorder: an open-label phase 1b/2a trial.Nature medicine · 2022Trial
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- Early autism detection: a review of emerging technologies, biomarkers, and explainable AI approaches.Molecular brain · 2025Review
- The Long and Winding Road to Understanding Autism.NeuroSci · 2025Review
- Recent Developments in the Application of Artificial Intelligence and Machine Learning in Early Screening and Diagnosis of Autism.Methods in molecular biology (Clifton, N.J.) · 2025Review
- AI-assisted early screening, diagnosis, and intervention for autism in young children.Frontiers in psychiatry · 2025Review
- A joint complex network and machine learning approach for the identification of discriminative gene communities in autistic brain.PloS one · 2025Article
- Ranking and Combining Latent Structured Predictive Scores without Labeled Data.IISE transactions · 2024Article
- Proximity analysis of native proteomes reveals phenotypic modifiers in a mouse model of autism and related neurodevelopmental conditions.Nature communications · 2024Article
- The Importance of Large-Scale Genomic Studies to Unravel Genetic Risk Factors for Autism.International journal of molecular sciences · 2024Review
- Article
- A network-based method for associating genes with autism spectrum disorder.Frontiers in bioinformatics · 2024Article
- Article
- Article
- Prioritizing de novo autism risk variants with calibrated gene- and variant-scoring models.Human genetics · 2022Article
- Integrating de novo and inherited variants in 42,607 autism cases identifies mutations in new moderate-risk genes.Nature genetics · 2022Article
- DeepND: Deep multitask learning of gene risk for comorbid neurodevelopmental disorders.Patterns (New York, N.Y.) · 2022Article
- SFARI genes and where to find them; modelling Autism Spectrum Disorder specific gene expression dysregulation with RNA-seq data.Scientific reports · 2022Article
- NeuroSCORE is a genome-wide omics-based model that identifies candidate disease genes of the central nervous system.Scientific reports · 2022Article
- Connecting phenotype to genotype: PheWAS-inspired analysis of autism spectrum disorder.Frontiers in human neuroscience · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Genetics has been one of the most powerful windows into the biology of autism spectrum disorder (ASD). It is estimated that a thousand or more genes may confer risk for ASD when functionally perturbed, however, only around 100 genes currently have sufficient evidence to be considered true "autism risk genes". Massive genetic studies are currently underway producing data to implicate additional genes. This approach - although necessary - is costly and slow-moving, making identification of putative ASD risk genes with existing data vital. Here, we approach autism risk gene discovery as a machine learning problem, rather than a genetic association problem, by using genome-scale data as predictors to identify new genes with similar properties to established autism risk genes. This ensemble method, forecASD, integrates brain gene expression, heterogeneous network data, and previous gene-level predictors of autism association into an ensemble classifier that yields a single score indexing evidence of each gene's involvement in the etiology of autism. We demonstrate that forecASD has substantially better performance than previous predictors of autism association in three independent trio-based sequencing studies. Studying forecASD prioritized genes, we show that forecASD is a robust indicator of a gene's involvement in ASD etiology, with diverse applications to gene discovery, differential expression analysis, eQTL prioritization, and pathway enrichment analysis.
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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.