ArticleNature genetics2024
Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction.
Article in Nature genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Replicability of unsupervised deep learning derived image phenotypes.bioRxiv : the preprint server for biology · 2026Article
- Genomics of drug target prioritization for complex diseases.Nature reviews. Genetics · 2026Review
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- The predicament of heritable confounders.Nature genetics · 2026Review
- Cardiovascular-kidney-metabolic syndrome: candidate subtypes and genetic risk factors.BMC medical genomics · 2026Article
- Estimating Ascending Aortic Diameter From the Electrocardiogram.Journal of the American College of Cardiology · 2026Article
- EvoApneaFormer: an IoT and prognostic evolutionary deep learning-based framework for real-time multi-event sleep apnea disorder detection and remote monitoring.Frontiers in bioengineering and biotechnology · 2026Article
- Unsupervised Phenotyping of Atrial Fibrillation Using Variational Autoencoders in a Critical Care Cohort: The ATLAS-AF Study.Clinical Medicine Insights. Cardiology · 2026Article
- Improving polygenic risk prediction performance by integrating electronic health records through phenotype embedding.American journal of human genetics · 2025Article
- PERADIGM: Phenotype embedding similarity-based rare disease gene mapping.PLoS genetics · 2025Article
- Heart rate variability associated with depression and lung decline in chronic obstructive pulmonary disease: Mechanisms and interventions.World journal of psychiatry · 2025Article
- The broad structure of psychopathology in the All of Us Research Program.Psychological medicine · 2025Article
- AI-powered programmable virtual humans toward human physiologically-based drug discovery.Drug discovery today · 2025Review
- An effective encoding of human medical conditions in disease space provides a versatile framework for deciphering disease associations.Quantitative biology (Beijing, China) · 2025Article
- Applying multimodal AI to physiological waveforms improves genetic prediction of cardiovascular traits.American journal of human genetics · 2025Article
- Deep Learning of Suboptimal Spirometry to Predict Respiratory Outcomes and Mortality.Research square · 2025Article
- Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers.Bioinformatics (Oxford, England) · 2024Article
- Accelerating Heritability, Genetic Correlation, and Genome-Wide Association Imaging Genetic Analyses in Complex Pedigrees.Human brain mapping · 2024Article
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Abstract
Although high-dimensional clinical data (HDCD) are increasingly available in biobank-scale datasets, their use for genetic discovery remains challenging. Here we introduce an unsupervised deep learning model, Representation Learning for Genetic Discovery on Low-Dimensional Embeddings (REGLE), for discovering associations between genetic variants and HDCD. REGLE leverages variational autoencoders to compute nonlinear disentangled embeddings of HDCD, which become the inputs to genome-wide association studies (GWAS). REGLE can uncover features not captured by existing expert-defined features and enables the creation of accurate disease-specific polygenic risk scores (PRSs) in datasets with very few labeled data. We apply REGLE to perform GWAS on respiratory and circulatory HDCD-spirograms measuring lung function and photoplethysmograms measuring blood volume changes. REGLE replicates known loci while identifying others not previously detected. REGLE are predictive of overall survival, and PRSs constructed from REGLE loci improve disease prediction across multiple biobanks. Overall, REGLE contain clinically relevant information beyond that captured by existing expert-defined features, leading to improved genetic discovery and disease prediction.
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