Binary Phenotype Classification

Binary Phenotype Classification is a supervised machine learning task in computational biology that involves categorizing biological entities into one of two distinct phenotypic classes based on observed features or measurements.

23

Publications

1.3K

Citations

159

Authors

72

Institutions

Publications per year

2017–2026

12

Authors

159

  1. AJ
    Adeniyi J. Adewale

    University of Alberta, Canada

    2 publications · 292 citations

    2

    h-index

  2. JD
    John D. Potter

    Fred Hutch Cancer Center, United States

    2 publications · 292 citations

    2

    h-index

  3. YY
    Yutaka Yasui

    University of Alberta, Canada

    2 publications · 292 citations

    2

    h-index

  4. ID
    Irina Dinu

    University of Alberta, Canada

    2 publications · 292 citations

    2

    h-index

  5. AM
    A.P. Mitchell

    Animal and Plant Health Agency, United Kingdom

    1 publications · 60 citations

    1

    h-index

1–5 of 159

Venues

  1. Addiction Biology

    1 publications · 50 citations

    1

    h-index

  2. BMC Medical Genomics

    1 publications · 10 citations

    1

    h-index

  3. Computer Methods and Programs in Biomedicine

    1 publications · 11 citations

    1

    h-index

  4. Cardiology and Cardiovascular Medicine

    1 publications · 12 citations

    1

    h-index

  5. Human Heredity

    1 publications · 21 citations

    1

    h-index

1–5