Phenotypic Abnormality Classification

Phenotypic Abnormality Classification is a computational method that categorizes observable traits or characteristics in biological entities by identifying deviations from normal phenotypic states within structured ontology frameworks.

48

Publications

4.5K

Citations

411

Authors

247

Institutions

Publications per year

2017–2026

13

Authors

411

  1. DV
    Daniela V. Luquetti

    University of Washington, United States

    3 publications · 93 citations

    3

    h-index

  2. CL
    Carrie L. Heike

    University of Washington, United States

    3 publications · 93 citations

    3

    h-index

  3. RT
    Rossella Tupler

    University of Modena and Reggio Emilia, Italy

    2 publications · 121 citations

    2

    h-index

  4. PN
    Peter N. Robinson

    Charité - Universitätsmedizin Berlin, Germany

    2 publications · 869 citations

    2

    h-index

  5. YN
    Yasuni Nakanuma

    Kanazawa University, Japan

    2 publications · 101 citations

    2

    h-index

1–5 of 411

Venues

  1. BMC Medical Genetics

    2 publications · 37 citations

    2

    h-index

  2. American Journal of Medical Genetics

    2 publications · 359 citations

    2

    h-index

  3. Journal of Neurology

    1 publications · 99 citations

    1

    h-index

  4. Australian Veterinary Journal

    1 publications · 12 citations

    1

    h-index

  5. Genes to Cells

    1 publications · 34 citations

    1

    h-index

1–5