Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology

Bonazzola, R. orcid.org/0000-0001-8811-2581, Ferrante, E., Ravikumar, N. orcid.org/0000-0003-0134-107X et al. (5 more authors) (2024) Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology. Nature Machine Intelligence, 6. pp. 291-306. ISSN 2522-5839

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Keywords: 46 Information and Computing Sciences; 40 Engineering; Networking and Information Technology R&D (NITRD); Human Genome; Biomedical Imaging; Genetics; Machine Learning and Artificial Intelligence; Cardiovascular; Heart Disease; 2.1 Biological and endogenous factors; Generic health relevance; Cardiovascular
Dates:
  • Published: March 2024
  • Published (online): 11 March 2024
  • Accepted: 25 January 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Biomedical & Health
The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Biomedical Imaging Science Dept (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 25 Jul 2024 13:05
Last Modified: 25 Jul 2024 13:05
Published Version: http://dx.doi.org/10.1038/s42256-024-00801-1
Status: Published
Publisher: Springer Nature
Identification Number: 10.1038/s42256-024-00801-1
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