Self-supervised Protein Representation Learning

Self-Supervised Protein Representation Learning is a computational framework that belongs to the class of unsupervised learning methods, specifically distinguished by its ability to extract meaningful amino acid sequence representations through pretext tasks designed on unlabeled protein data without external annotation.

40

Publications

6K

Citations

175

Authors

76

Institutions

Publications per year

2017–2026

38

Authors

175

  1. KY
    Kevin Yang

    Microsoft (United States), United States

    2 publications · 114 citations

    2

    h-index

  2. JH
    James H. R. Farmery

    University of Oxford, United Kingdom

    2 publications · 176 citations

    2

    h-index

  3. RY
    Ronghui You

    Fudan University, China

    2 publications · 106 citations

    2

    h-index

  4. XC
    Xi Chen

    Shandong Institute of Automation, China

    2 publications · 769 citations

    2

    h-index

  5. YL
    Yunjia Liu

    The University of Queensland, Australia

    2 publications · 106 citations

    2

    h-index

1–5 of 175

Venues

  1. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 8 publications · 1.3K citations

    8

    h-index

  2. Bioinformatics

    6 publications · 1.3K citations

    6

    h-index

  3. Briefings in Bioinformatics

    3 publications · 99 citations

    3

    h-index

  4. arXiv (Cornell University)

    Repository · 3 publications · 81 citations

    3

    h-index

  5. Protein Science

    PS · 2 publications · 46 citations

    2

    h-index

1–5