High-throughput Interaction Data Analysis

High-Throughput Interaction Data Analysis is a computational and statistical methodology for systematically identifying, integrating, and interpreting large-scale protein-protein interaction datasets generated by automated experimental techniques, distinguished by its capacity to manage voluminous, heterogeneous, and noisy interaction data to infer biological function, network topology, and functional modules within interactome networks.

283

Publications

91.8K

Citations

1.4K

Authors

500

Institutions

Publications per year

2017–2026

68

Authors

1.4K

  1. ZY
    Zhu‐Hong You

    Xinjiang Technical Institute of Physics & Chemistry, China

    8 publications · 566 citations

    8

    h-index

  2. TI
    Trey Ideker

    University of California San Diego, United States

    6 publications · 55K citations

    6

    h-index

  3. SN
    See-Kiong Ng

    Institute for Infocomm Research, Singapore

    5 publications · 262 citations

    5

    h-index

  4. RL
    Russell L. Finley

    Wayne State University, United States

    5 publications · 705 citations

    5

    h-index

  5. MT
    Mike Tyers

    Lunenfeld-Tanenbaum Research Institute, Canada

    5 publications · 2.3K citations

    5

    h-index

1–5 of 1.4K

Venues

  1. Bioinformatics

    30 publications · 3.2K citations

    24

    h-index

  2. BMC Bioinformatics

    24 publications · 1.9K citations

    20

    h-index

  3. Nucleic Acids Research

    23 publications · 8.1K citations

    20

    h-index

  4. Genome biology

    10 publications · 1.2K citations

    10

    h-index

  5. PLoS ONE

    9 publications · 284 citations

    9

    h-index

1–5