ArticleNature machine intelligence2024
Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology.
Article in Nature machine intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 23 citations in OpenAlex.
- Cardiac remodelling and dysfunction in cancer patients receiving cardiotoxic therapies: proteomic and metabolomic profiling.European heart journal · 2026Article
- Review
- Hundreds of cardiac MRI traits derived using 3D diffusion autoencoders share a common genetic architecture.Nature communications · 2026Article
- Four-dimensional left ventricular motion clustering reveals cardiovascular phenotypes at population scale.Scientific reports · 2026Article
- Multi-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traits.Nature cardiovascular research · 2026Article
- Replicability of unsupervised deep learning derived image phenotypes.bioRxiv : the preprint server for biology · 2026Article
- Learning dynamics of unsupervised deep learning reveal epoch-specific genetic architectures of brain morphology.bioRxiv : the preprint server for biology · 2026Article
- Graph-Convolutional-Beta-VAE for synthetic abdominal aortic aneurysm generation.Medical & biological engineering & computing · 2026Article
- Unsupervised machine learning integrates genomic variants and EMR to unravel mechanisms of brain hemorrhage and epilepsy as early indicators of Alzheimer's in down syndrome.Alzheimer's research & therapy · 2025Article
- Characterization of SPTLC2 as a key driver promoting microglial activation and energy metabolism reprogramming after ischemic stroke through bulk and single-cell analyses combined with experimental validation.Cell biology and toxicology · 2025Article
- Ophthalmic imaging as a measure of cardiovascular and neurological health: a multi-omic analysis of deep-learning derived phenotypes.medRxiv : the preprint server for health sciences · 2025Article
- Genome-wide association analysis of composite sleep health scores in 413,904 individuals.Communications biology · 2025Article
- A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics.Nature machine intelligence · 2025Article
- Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers.Bioinformatics (Oxford, England) · 2024Article
- TransferGWAS of T1-weighted brain MRI data from UK Biobank.PLoS genetics · 2024Article
- Genetic basis of right and left ventricular heart shape.Nature communications · 2024Article
- Relationships between heart shape, function, and disease in 38,858 UK biobank participants.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
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Authors and funding
8 authors at 4 institutions in 4 countries.
Funding
Abstract
Recent genome-wide association studies have successfully identified associations between genetic variants and simple cardiac morphological parameters derived from cardiac magnetic resonance images. However, the emergence of large databases, including genetic data linked to cardiac magnetic resonance facilitates the investigation of more nuanced patterns of cardiac shape variability than those studied so far. Here we propose a framework for gene discovery coined unsupervised phenotype ensembles. The unsupervised phenotype ensemble builds a redundant yet highly expressive representation by pooling a set of phenotypes learnt in an unsupervised manner, using deep learning models trained with different hyperparameters. These phenotypes are then analysed via genome-wide association studies, retaining only highly confident and stable associations across the ensemble. We applied our approach to the UK Biobank database to extract geometric features of the left ventricle from image-derived three-dimensional meshes. We demonstrate that our approach greatly improves the discoverability of genes that influence left ventricle shape, identifying 49 loci with study-wide significance and 25 with suggestive significance. We argue that our approach would enable more extensive discovery of gene associations with image-derived phenotypes for other organs or image modalities.
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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.