Deep Learning For Sequencing

Deep Learning for Sequencing is a computational methodology within the field of de novo peptide sequencing that employs deep neural networks to directly infer amino acid sequences from tandem mass spectrometry data without reliance on reference databases.

107

Publications

9.9K

Citations

524

Authors

238

Institutions

Publications per year

2017–2026

86

Authors

524

  1. RL
    Ruibang Luo

    University of Hong Kong, Hong Kong SAR China

    4 publications · 416 citations

    4

    h-index

  2. JM
    Jakub M. Bartoszewicz

    Hasso Plattner Institute, Germany

    4 publications · 162 citations

    4

    h-index

  3. BY
    Bernhard Y. Renard

    Robert Koch Institute, Germany

    4 publications · 162 citations

    4

    h-index

  4. BS
    Baozhen Shan

    University of Waterloo, Canada

    3 publications · 868 citations

    3

    h-index

  5. TL
    Tak‐Wah Lam

    University of Hong Kong, Hong Kong SAR China

    3 publications · 372 citations

    3

    h-index

1–5 of 524

Venues

  1. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 9 publications · 248 citations

    9

    h-index

  2. BMC Bioinformatics

    8 publications · 384 citations

    8

    h-index

  3. Bioinformatics

    8 publications · 669 citations

    8

    h-index

  4. Briefings in Bioinformatics

    6 publications · 172 citations

    7

    h-index

  5. Nature Communications

    7 publications · 882 citations

    7

    h-index

1–5