Sequence-function Inference

Sequence-Function Inference is a computational or statistical method that, within the context of genotype-phenotype coupling, identifies the relationship between biological sequence variants and their functional phenotypic outcomes by modeling how specific nucleotide or amino acid changes give rise to observable molecular or cellular functions.

44

Publications

3.5K

Citations

256

Authors

103

Institutions

Publications per year

2017–2026

25

Authors

256

  1. PA
    Philip A. Romero

    University of Wisconsin–Madison, United States

    3 publications · 106 citations

    3

    h-index

  2. CR
    Chase R. Freschlin

    University of Wisconsin–Madison, United States

    3 publications · 106 citations

    3

    h-index

  3. DB
    David Bikard

    Institut Pasteur, France

    2 publications · 236 citations

    2

    h-index

  4. JM
    Joshua Moller

    University of Chicago, United States

    2 publications · 47 citations

    2

    h-index

  5. SD
    Sameer D’Costa

    University of Wisconsin–Madison, United States

    2 publications · 62 citations

    2

    h-index

1–5 of 256

Venues

  1. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 5 publications · 779 citations

    5

    h-index

  2. Nature Communications

    4 publications · 389 citations

    4

    h-index

  3. Bioinformatics

    3 publications · 169 citations

    3

    h-index

  4. PLoS Computational Biology

    2 publications · 223 citations

    2

    h-index

  5. Proceedings of the National Academy of Sciences

    PNAS · 2 publications · 62 citations

    2

    h-index

1–5