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Assessment of community efforts to advance network-based prediction of protein–protein interactions

Author

Listed:
  • Xu-Wen Wang

    (Brigham and Women’s Hospital and Harvard Medical School)

  • Lorenzo Madeddu

    (Translational and Precision Medicine Department Sapienza University of Rome)

  • Kerstin Spirohn

    (Dana-Farber Cancer Institute
    Blavatnik Institute, Harvard Medical School
    Dana-Farber Cancer Institute)

  • Leonardo Martini

    (Sapienza University of Rome)

  • Adriano Fazzone

    (CENTAI Institute)

  • Luca Becchetti

    (Sapienza University of Rome)

  • Thomas P. Wytock

    (Northwestern University)

  • István A. Kovács

    (Northwestern University
    Northwestern University)

  • Olivér M. Balogh

    (Semmelweis University)

  • Bettina Benczik

    (Semmelweis University
    Pharmahungary Group)

  • Mátyás Pétervári

    (Semmelweis University)

  • Bence Ágg

    (Semmelweis University
    Pharmahungary Group)

  • Péter Ferdinandy

    (Semmelweis University
    Pharmahungary Group)

  • Loan Vulliard

    (CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences
    University of Vienna)

  • Jörg Menche

    (CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences
    University of Vienna
    University of Vienna)

  • Stefania Colonnese

    (University of Rome “Sapienza”)

  • Manuela Petti

    (Sapienza University of Rome)

  • Gaetano Scarano

    (University of Rome “Sapienza”)

  • Francesca Cuomo

    (University of Rome “Sapienza”)

  • Tong Hao

    (Dana-Farber Cancer Institute
    Blavatnik Institute, Harvard Medical School
    Dana-Farber Cancer Institute)

  • Florent Laval

    (Dana-Farber Cancer Institute
    Blavatnik Institute, Harvard Medical School
    Dana-Farber Cancer Institute
    University of Liège)

  • Luc Willems

    (University of Liège
    University of Liège)

  • Jean-Claude Twizere

    (University of Liège
    University of Liège)

  • Marc Vidal

    (Dana-Farber Cancer Institute
    Blavatnik Institute, Harvard Medical School)

  • Michael A. Calderwood

    (Dana-Farber Cancer Institute
    Blavatnik Institute, Harvard Medical School
    Dana-Farber Cancer Institute)

  • Enrico Petrillo

    (Brigham and Women’s Hospital and Harvard Medical School
    Brigham and Women’s Hospital)

  • Albert-László Barabási

    (Brigham and Women’s Hospital and Harvard Medical School
    Northeastern University
    Central European University)

  • Edwin K. Silverman

    (Brigham and Women’s Hospital and Harvard Medical School)

  • Joseph Loscalzo

    (Brigham and Women’s Hospital and Harvard Medical School)

  • Paola Velardi

    (Translational and Precision Medicine Department Sapienza University of Rome)

  • Yang-Yu Liu

    (Brigham and Women’s Hospital and Harvard Medical School
    University of Illinois at Urbana-Champaign)

Abstract

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

Suggested Citation

  • Xu-Wen Wang & Lorenzo Madeddu & Kerstin Spirohn & Leonardo Martini & Adriano Fazzone & Luca Becchetti & Thomas P. Wytock & István A. Kovács & Olivér M. Balogh & Bettina Benczik & Mátyás Pétervári & Be, 2023. "Assessment of community efforts to advance network-based prediction of protein–protein interactions," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37079-7
    DOI: 10.1038/s41467-023-37079-7
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    References listed on IDEAS

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