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Comparative assessment of large-scale data sets of protein–protein interactions

Author

Listed:
  • Christian von Mering

    (European Molecular Biology Laboratory)

  • Roland Krause

    (Cellzome AG)

  • Berend Snel

    (European Molecular Biology Laboratory)

  • Michael Cornell

    (University of Manchester)

  • Stephen G. Oliver

    (University of Manchester)

  • Stanley Fields

    (University of Washington)

  • Peer Bork

    (European Molecular Biology Laboratory)

Abstract

Comprehensive protein–protein interaction maps promise to reveal many aspects of the complex regulatory network underlying cellular function. Recently, large-scale approaches have predicted many new protein interactions in yeast. To measure their accuracy and potential as well as to identify biases, strengths and weaknesses, we compare the methods with each other and with a reference set of previously reported protein interactions.

Suggested Citation

  • Christian von Mering & Roland Krause & Berend Snel & Michael Cornell & Stephen G. Oliver & Stanley Fields & Peer Bork, 2002. "Comparative assessment of large-scale data sets of protein–protein interactions," Nature, Nature, vol. 417(6887), pages 399-403, May.
    • Handle: RePEc:nat:nature:v:417:y:2002:i:6887:d:10.1038_nature750
    DOI: 10.1038/nature750
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    Cited by:

    1. Mingshuo Nie & Dongming Chen & Dongqi Wang, 2022. "Graph Embedding Method Based on Biased Walking for Link Prediction," Mathematics, MDPI, vol. 10(20), pages 1-13, October.
    2. Michael A. Skinnider & Mopelola O. Akinlaja & Leonard J. Foster, 2023. "Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. D’Ambra, Pasqua & Vassilevski, Panayot S. & Cutillo, Luisa, 2023. "Extending bootstrap AMG for clustering of attributed graphs," Applied Mathematics and Computation, Elsevier, vol. 447(C).
    4. Haji Gul & Feras Al-Obeidat & Adnan Amin & Fernando Moreira & Kaizhu Huang, 2022. "Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    5. Zhang, Qi & Li, Meizhu, 2022. "A betweenness structural entropy of complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).

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