Semi-supervised Phenotype Discovery
Semi-Supervised Phenotype Discovery is a computational approach in biomedical informatics that integrates a limited set of labeled phenotypic data with a larger pool of unlabeled data to identify novel, clinically meaningful subgroups of traits or diseases, distinguished from unsupervised and fully supervised methods by its strategic use of partial expert-annotated guidance within an otherwise data-driven clustering framework.
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530
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76
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40
Institutions
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2017–2026
10
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Collins Kipngetich Tanui, Shraddha Karanth, Patrick Murigu Kamau Njage et al. · LWT · 2021 · 37 citations · Full text
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University of Pennsylvania
Philadelphia, United States
3 h-index
6 publications
536 citations
Translational Therapeutics (United States)
Arlington, United States
2 h-index
2 publications
247 citations
The University of Texas at Austin
Austin, United States
1 h-index
2 publications
46 citations
Wellcome Centre for Integrative Neuroimaging
Oxford, United Kingdom
1 h-index
3 publications
66 citations

Institut Gustave Roussy
Villejuif, France
1 h-index
1 publications
14 citations
Mayo Clinic
Rochester, United States
1 h-index
1 publications
23 citations

Radboud University Nijmegen
Nijmegen, The Netherlands
1 h-index
1 publications
22 citations
West Virginia University
Morgantown, United States
1 h-index
8 publications
184 citations
1–8 of 40
2 publications · 43 citations
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JAMIA · 1 publications · 27 citations
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Repository · 1 publications · 23 citations
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1 publications · 18 citations
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1 publications · 17 citations
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