Machine Learning-based Annotation

Machine Learning-Based Annotation is a computational approach to protein function prediction that employs machine learning models to infer functional labels from biological data, distinguishing itself from rule-based or homology-based methods by leveraging patterns learned from annotated training examples.

127

Publications

9.7K

Citations

607

Authors

263

Institutions

Publications per year

2017–2026

56

Authors

607

  1. GY
    Guoxian Yu

    Southwest University, China

    4 publications · 137 citations

    4

    h-index

  2. CO
    Christine Orengo

    University College London, United Kingdom

    3 publications · 106 citations

    3

    h-index

  3. JY
    Jieping Ye

    Arizona State University, United States

    3 publications · 143 citations

    3

    h-index

  4. BR
    Burkhard Rost

    Columbia University, United States

    3 publications · 262 citations

    3

    h-index

  5. GP
    Gajendra P. S. Raghava

    Institute of Microbial Technology, India

    3 publications · 462 citations

    3

    h-index

1–5 of 607

Venues

  1. Bioinformatics

    16 publications · 1.9K citations

    15

    h-index

  2. BMC Bioinformatics

    10 publications · 678 citations

    10

    h-index

  3. Nucleic Acids Research

    6 publications · 763 citations

    6

    h-index

  4. 6

    h-index

  5. PLoS Computational Biology

    4 publications · 111 citations

    4

    h-index

1–5