Computational Variant Prediction

Computational Variant Prediction is a scholarly methodological contribution that employs algorithmic, statistical, or machine learning approaches to infer the functional or clinical impact of genetic variants, distinguished from other variant classification strategies by its reliance on in silico modeling rather than empirical or population-based evidence.

203

Publications

29.9K

Citations

1.2K

Authors

610

Institutions

Publications per year

2017–2026

128

Authors

1.2K

  1. MV
    Mauno Vihinen

    9 publications · 922 citations

    9

    h-index

  2. EC
    Emidio Capriotti

    University of Bologna, Italy

    8 publications · 1.9K citations

    8

    h-index

  3. JA
    Joseph A. Marsh

    University of Edinburgh, United Kingdom

    7 publications · 630 citations

    7

    h-index

  4. DC
    D.N. Cooper

    Cardiff University, United Kingdom

    7 publications · 3K citations

    7

    h-index

  5. YB
    Yana Bromberg

    Rutgers, The State University of New Jersey, United States

    7 publications · 2K citations

    7

    h-index

1–5 of 1.2K

Venues

  1. Human Mutation

    22 publications · 2.7K citations

    18

    h-index

  2. Nucleic Acids Research

    13 publications · 6.2K citations

    13

    h-index

  3. Scientific Reports

    13 publications · 764 citations

    11

    h-index

  4. Bioinformatics

    8 publications · 1.3K citations

    8

    h-index

  5. PLoS ONE

    6 publications · 435 citations

    6

    h-index

1–5