Outcome Phenotype Classification
Outcome Phenotype Classification is a supervised machine learning task that specifically involves the prediction of observable phenotypic outcomes from biological or clinical data, distinguished from other phenotype classification tasks by its focus on endpoints or resultant traits rather than intermediate or diagnostic phenotypes.
8
Publications
703
Citations
55
Authors
24
Institutions
Publications per year
2017–2026
2
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Multi-omics data integration by generative adversarial network
Khandakar Tanvir Ahmed, Jiao Sun, Sze Cheng et al. · Bioinformatics · 2021 · 90 citations · Full text
Quality-of-care research in mental health: responding to the challenge.
McGlynn Ea, Norquist Gs, Wells Kb et al. · PubMed · 1988 · 74 citations
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University of Toronto
Toronto, Canada
1 h-index
1 publications
130 citations
University Medical Center Utrecht
Utrecht, The Netherlands
1 h-index
6 publications
252 citations
The University of Texas MD Anderson Cancer Center
Houston, United States
1 h-index
4 publications
520 citations
Mayo Clinic in Arizona
Scottsdale, United States
1 h-index
2 publications
260 citations
University of California San Diego
San Diego, United States
1 h-index
7 publications
903 citations
Antwerp University Hospital
Antwerp, Belgium
1 h-index
4 publications
92 citations

University of British Columbia
Vancouver, Canada
1 h-index
2 publications
260 citations

Alliant International University
San Diego, United States
1 h-index
7 publications
903 citations
1–8 of 24
1 publications · 129 citations
1
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1 publications · 200 citations
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Repository · 1 publications · 74 citations
1
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1 publications · 23 citations
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JHS · 1 publications · 15 citations
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