Sequence-based Distance Prediction

Sequence-Based Distance Prediction is a computational method in structural bioinformatics that predicts pairwise residue-residue distances in a protein structure exclusively from its amino acid sequence, distinguishing itself from structure-based or coevolutionary methods by relying solely on sequence-derived features and machine learning models.

50

Publications

3.6K

Citations

201

Authors

96

Institutions

Publications per year

2017–2026

22

Authors

201

  1. DB
    Dongbo Bu

    Institute of Computing Technology, China

    3 publications · 178 citations

    3

    h-index

  2. JZ
    Jianwei Zhu

    University of Chinese Academy of Sciences, China

    3 publications · 178 citations

    3

    h-index

  3. LK
    Lupeng Kong

    Institute of Theoretical Physics, China

    2 publications · 99 citations

    2

    h-index

  4. SJ
    Samuel J. Pellock

    University of Washington, United States

    2 publications · 692 citations

    2

    h-index

  5. FJ
    Fusong Ju

    Chinese Academy of Sciences, China

    2 publications · 99 citations

    2

    h-index

1–5 of 201

Venues

  1. Bioinformatics

    9 publications · 520 citations

    9

    h-index

  2. BMC Bioinformatics

    5 publications · 209 citations

    5

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 4 publications · 143 citations

    4

    h-index

  4. Protein Engineering Design and Selection

    PEDS · 3 publications · 600 citations

    3

    h-index

  5. PubMed

    Repository · 2 publications · 51 citations

    2

    h-index

1–5