Machine Learning For Threading

Machine Learning for Threading is a computational methodology within the field of protein structure prediction that employs machine learning models to infer the optimal structural template for a target protein sequence by learning from the patterns of sequence-structure compatibility in known protein folds.

8

Publications

310

Citations

16

Authors

11

Institutions

Publications per year

2017–2026

1

Authors

16

  1. JX
    Jinbo Xu

    Toyota Technological Institute at Chicago, United States

    3 publications · 119 citations

    3

    h-index

  2. MB
    Michał Bryliński

    Louisiana State University, United States

    3 publications · 148 citations

    3

    h-index

  3. LY
    Libo Yu

    Fujian Institute of Oceanography, China

    2 publications · 43 citations

    2

    h-index

  4. DS
    Dale Schuurmans

    University of Alberta, Canada

    1 publications · 11 citations

    1

    h-index

  5. AM
    Amos Maritan

    University of Padua, Italy

    1 publications · 32 citations

    1

    h-index

1–5 of 16

Venues

  1. Journal of Computer Science & Systems Biology

    1 publications · 12 citations

    1

    h-index

  2. Humana Press eBooks

    Ebook platform · 1 publications · 32 citations

    1

    h-index

  3. Lecture notes in computer science

    LNCS · Book series · 1 publications · 76 citations

    1

    h-index

  4. Proceedings of the National Academy of Sciences

    PNAS · 1 publications · 32 citations

    1

    h-index

  5. Proteins Structure Function and Bioinformatics

    1 publications · 101 citations

    1

    h-index

1–5