Knowledge Graph-based Repurposing

Knowledge Graph-Based Repurposing is a computational approach within drug repurposing that leverages structured knowledge graphs—integrating heterogeneous biological, chemical, and phenotypic data through semantic relationships—to systematically identify novel therapeutic indications for existing drugs by inferring non-obvious connections between drugs, targets, and diseases.

74

Publications

4.3K

Citations

468

Authors

189

Institutions

Publications per year

2017–2026

72

Variants

Knowledge Graph-based Drug Repurposing

Authors

468

  1. FC
    Feixiong Cheng

    Cleveland Clinic Lerner College of Medicine, United States

    4 publications · 479 citations

    4

    h-index

  2. RZ
    Rui Zhang

    University of Minnesota, United States

    4 publications · 287 citations

    4

    h-index

  3. XZ
    Xiangxiang Zeng

    Hunan University, China

    3 publications · 450 citations

    3

    h-index

  4. GK
    George Karypis

    University of Minnesota, United States

    3 publications · 318 citations

    3

    h-index

  5. CC
    Chao Che

    Dalian University of Technology, China

    3 publications · 147 citations

    3

    h-index

1–5 of 468

Venues

  1. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 6 publications · 330 citations

    6

    h-index

  2. Scientific Reports

    6 publications · 199 citations

    6

    h-index

  3. arXiv (Cornell University)

    Repository · 4 publications · 92 citations

    4

    h-index

  4. Bioinformatics

    4 publications · 113 citations

    4

    h-index

  5. BMC Bioinformatics

    3 publications · 66 citations

    3

    h-index

1–5