ArticleGenome medicine2020
An unsupervised learning approach to identify novel signatures of health and disease from multimodal data.
Article in Genome medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 54 citations in OpenAlex.
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- Identifying the Ratio of Circulating Cinnamoylglycine to Isoleucine as a Biomarker Linking Diet Quality, Microbial Metabolism, and Type 2 Diabetes Risk in the Coronary Artery Risk Development in Young Adults (CARDIA) Cohorts.The Journal of nutrition · 2026Article
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- Artificial intelligence in the management of metabolic disorders: a comprehensive review.Journal of endocrinological investigation · 2025Review
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Dietary energy and protein gradients drive metabolic adaptation in growing-finishing yaks on the Qinghai-Tibet plateau.Animal nutrition (Zhongguo xu mu shou yi xue hui) · 2025Article
- Integration of two-dimensional echocardiography: A novel risk indicator for ST-segment elevation myocardial infarction.ESC heart failure · 2024Article
- Non-targeted metabolomics analysis of fermented traditional Chinese medicine and its impact on growth performance, serum biochemistry, and intestinal microbiome of weaned lambs.Scientific reports · 2024Article
- Identifying diseases symptoms and general rules using supervised and unsupervised machine learning.Scientific reports · 2024Article
- Data-driven clustering approach to identify novel clusters of high cognitive impairment risk among Chinese community-dwelling elderly people with normal cognition: A national cohort study.Journal of global health · 2024Article
- Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management.Cell metabolism · 2024Review
- Article
- Artificial intelligence for dementia prevention.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Tracking longitudinal biomarkers in burn patients with sepsis and acute kidney injury: an unsupervised clustering approach.European journal of medical research · 2023Article
- Artificial Intelligence Technologies in Cardiology.Journal of cardiovascular development and disease · 2023Review
- Data-driven precision medicine through the analysis of biological functional modules.Cell reports. Medicine · 2022Article
- A population-based study of precision health assessments using multi-omics network-derived biological functional modules.Cell reports. Medicine · 2022Article
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
Corrections and comments
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Authors and funding
20 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundModern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. Integrated analysis of data from different modalities has the potential of uncovering novel biomarkers and disease signatures.
methodsWe collected 1385 data features from diverse modalities, including metabolome, microbiome, genetics, and advanced imaging, from 1253 individuals and from a longitudinal validation cohort of 1083 individuals. We utilized a combination of unsupervised machine learning methods to identify multimodal biomarker signatures of health and disease risk.
resultsOur method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers. Stratification of individuals based on the signatures of these biomarkers identified distinct subsets of individuals with similar health statuses. Subset membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and body mass index. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential biomarker for both gut microbiome health and lean mass percentage. We identified potential early signatures for hypertension and a poor metabolic health outcome. Additionally, we found novel associations between a uremic toxin, p-cresol sulfate, and the abundance of the microbiome genera Intestinimonas and an unclassified genus in the Erysipelotrichaceae family.
conclusionsOur methodology and results demonstrate the potential of multimodal data integration, from the identification of novel biomarker signatures to a data-driven stratification of individuals into disease subtypes and stages-an essential step towards personalized, preventative health risk assessment.
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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.