Deep Learning Annotation Models

Deep Learning Annotation Models is a computational annotation framework that leverages deep neural architectures to automatically infer and assign functional labels to biological sequences by learning hierarchical feature representations from large-scale genomic data.

207

Publications

15.3K

Citations

1.2K

Authors

497

Institutions

Publications per year

2017–2026

193

Authors

1.2K

  1. SF
    Sanja Fidler

    University of Toronto, Canada

    4 publications · 768 citations

    4

    h-index

  2. AK
    Amlan Kar

    Indian Institute of Science Bangalore, India

    4 publications · 768 citations

    4

    h-index

  3. KY
    Ke Yan

    National Institutes of Health Clinical Center, United States

    4 publications · 757 citations

    4

    h-index

  4. HL
    Huan Ling

    University of Toronto, Canada

    4 publications · 768 citations

    4

    h-index

  5. RM
    Ronald M. Summers

    National Institutes of Health Clinical Center, United States

    3 publications · 672 citations

    3

    h-index

1–5 of 1.2K

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 358 citations

    12

    h-index

  2. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 7 publications · 253 citations

    7

    h-index

  3. Scientific Reports

    7 publications · 205 citations

    7

    h-index

  4. Bioinformatics

    7 publications · 873 citations

    7

    h-index

  5. Nature Communications

    6 publications · 540 citations

    6

    h-index

1–5