ArticleNature computational science2023
The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci.
Article in Nature computational science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 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
7 citing papers in PubMed, 7 citations in OpenAlex.
- Clinically-applicable prediction of hospital stay and patient similarity retrieval in paediatric cardiology using machine learning.Nature communications · 2026Article
- Modeling psychopathology in high-dimensional vector space using the high-dimensional symptom space (HDSS) model can operationalize precision psychiatry in US adolescents.Scientific reports · 2025Article
- An effective encoding of human medical conditions in disease space provides a versatile framework for deciphering disease associations.Quantitative biology (Beijing, China) · 2025Article
- Digital twins as global learning health and disease models for preventive and personalized medicine.Genome medicine · 2025Review
- Linking dietary fiber to human malady through cumulative profiling of microbiota disturbance.iMeta · 2025Article
- Progress and opportunities of foundation models in bioinformatics.Briefings in bioinformatics · 2024Review
- The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci.Nature computational science · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
20 authors at 10 institutions in 6 countries.
Funding
Abstract
Human diseases are traditionally studied as singular, independent entities, limiting researchers' capacity to view human illnesses as dependent states in a complex, homeostatic system. Here, using time-stamped clinical records of over 151 million unique Americans, we construct a disease representation as points in a continuous, high-dimensional space, where diseases with similar etiology and manifestations lie near one another. We use the UK Biobank cohort, with half a million participants, to perform a genome-wide association study of newly defined human quantitative traits reflecting individuals' health states, corresponding to patient positions in our disease space. We discover 116 genetic associations involving 108 genetic loci and then use ten disease constellations resulting from clustering analysis of diseases in the embedding space, as well as 30 common diseases, to demonstrate that these genetic associations can be used to robustly predict various morbidities.
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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.