Data-driven Pathway Reconstruction

Data-Driven Pathway Reconstruction is a computational systems biology approach that reconstructs biological pathways by integrating and analyzing high-throughput experimental data to infer molecular interactions and functional relationships, distinguished from knowledge-based or model-driven methods by its reliance on empirical data as the primary source for pathway inference.

27

Publications

4.3K

Citations

135

Authors

64

Institutions

Publications per year

2017–2026

7

Authors

135

  1. MK
    Minoru Kanehisa

    Kyoto University, Japan

    2 publications · 3.2K citations

    2

    h-index

  2. AG
    Anthony Gitter

    University of Wisconsin–Madison, United States

    2 publications · 121 citations

    2

    h-index

  3. SC
    Shao‐shan Carol Huang

    Salk Institute for Biological Studies, United States

    2 publications · 200 citations

    2

    h-index

  4. NT
    Nurcan Tunçbağ

    Koç University, Türkiye

    2 publications · 200 citations

    2

    h-index

  5. EF
    Ernest Fraenkel

    Massachusetts Institute of Technology, United States

    2 publications · 200 citations

    2

    h-index

1–5 of 135

Venues

  1. BMC Bioinformatics

    4 publications · 192 citations

    4

    h-index

  2. PubMed

    Repository · 4 publications · 142 citations

    4

    h-index

  3. Nucleic Acids Research

    3 publications · 3.4K citations

    3

    h-index

  4. BMC Systems Biology

    2 publications · 60 citations

    2

    h-index

  5. Bioinformatics

    2 publications · 120 citations

    2

    h-index

1–5