ArticleBioinformatics (Oxford, England)2023
Automated machine learning for genome wide association studies.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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.
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Who cites it
11 citing papers in PubMed.
- Integrating multi-layer perceptron and random forest in an ensemble framework for improved genomic prediction accuracy and SHAP-derived interpretability of residual feed intake in cattle.Journal of animal science and biotechnology · 2026Article
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- Artificial Intelligence in genomics: a comprehensive survey of methods, resources, challenges, and prospects.Briefings in bioinformatics · 2026Review
- Transformer-based InsightGWAS improves GERD genetic discovery via pretraining on GWAS for major depressive disorder.Communications biology · 2026Article
- Transformer-based deep learning enhances discovery in migraine GWAS.Nature communications · 2025Article
- The Genetic Data Market: Institutional Governance of Academic/Industry Research Partnerships for the Public Good.The American journal of bioethics : AJOB · 2025Article
- Predicting natural variation in the yeast phenotypic landscape with machine learning.Molecular systems biology · 2025Article
- From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care.Biomedicines · 2025Review
- Identifying sepsis susceptibility genes in post-surgical patients using an artificial intelligence approach.Frontiers in medicine · 2025Article
- AutoXAI4Omics: an automated explainable AI tool for omics and tabular data.Briefings in bioinformatics · 2024Article
- Single-cell transcriptome analysis revealed heterogeneity in glycolysis and identified IGF2 as a therapeutic target for ovarian cancer subtypes.BMC cancer · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
motivationGenome-wide association studies (GWAS) present several computational and statistical challenges for their data analysis, including knowledge discovery, interpretability, and translation to clinical practice.
resultsWe develop, apply, and comparatively evaluate an automated machine learning (AutoML) approach, customized for genomic data that delivers reliable predictive and diagnostic models, the set of genetic variants that are important for predictions (called a biosignature), and an estimate of the out-of-sample predictive power. This AutoML approach discovers variants with higher predictive performance compared to standard GWAS methods, computes an individual risk prediction score, generalizes to new, unseen data, is shown to better differentiate causal variants from other highly correlated variants, and enhances knowledge discovery and interpretability by reporting multiple equivalent biosignatures. AVAILABILITY AND IMPLEMENTATION: Code for this study is available at: https://github.com/mensxmachina/autoML-GWAS. JADBio offers a free version at: https://jadbio.com/sign-up/. SNP data can be downloaded from the EGA repository (https://ega-archive.org/). PRS data are found at: https://www.aicrowd.com/challenges/opensnp-height-prediction. Simulation data to study population structure can be found at: https://easygwas.ethz.ch/data/public/dataset/view/1/.
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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.