ArticleNature communications2024
Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed, 43 citations in OpenAlex.
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- Applications of artificial intelligence and computational approaches to imaging for hypertension identification, phenotyping, and outcome prediction: a systematic review.European heart journal. Digital health · 2026Review
- AI-Assisted Molecular Biosensors: Design Strategies for Wearable and Real-Time Monitoring.International journal of molecular sciences · 2026Review
- Five tenets for advancing evidence-based precision medicine.Nature medicine · 2026Review
- Multi-organ AI endophenotypes chart the heterogeneity of brain, eye and heart pan-disease.Nature. Mental health · 2026Article
- Mutual learning for joint disease detection and severity prediction reveals multimodal pathogenesis for neurodegenerative disorders.Bioinformatics (Oxford, England) · 2026Article
- Mapping Alzheimer's disease heterogeneity through exploratory unsupervised learning.Frontiers in aging neuroscience · 2026Article
- Integrated approaches to geriatric mental disorders: pathophysiology, interventions, and precision medicine horizons.Frontiers in psychiatry · 2026Review
- Subtyping Autism Spectrum Disorder With a Population Graph-Based Dual Autoencoder: Revealing Two Distinct Biotypes.CNS neuroscience & therapeutics · 2025Article
- Distinct neuroimaging subtypes of ADHD among adolescents based on semi-supervised learning.Translational psychiatry · 2025Article
- Integration of Genetic and Imaging Data for Alzheimer's Disease Diagnosis and Interpretation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- The impact of artificial intelligence on biomarker discovery.Emerging topics in life sciences · 2025Article
- Towards a multi-organ, multi-omics medical digital twin.Nature biomedical engineering · 2025Article
- GPS: Harnessing data fusion strategies to improve the accuracy of machine learning-based genomic and phenotypic selection.Plant communications · 2025Article
- Applying multimodal AI to physiological waveforms improves genetic prediction of cardiovascular traits.American journal of human genetics · 2025Article
- A genetically informed brain atlas for enhancing brain imaging genomics.Nature communications · 2025Article
- Pioneering new paths: the role of generative modelling in neurological disease research.Pflugers Archiv : European journal of physiology · 2025Review
- Generative AI Models in Time-Varying Biomedical Data: Scoping Review.Journal of medical Internet research · 2025Article
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28 authors at 13 institutions in 4 countries.
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Abstract
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.
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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.