Articles producció científica> Enginyeria Química

A Network Inference Method for Large-Scale Unsupervised Identification of Novel Drug-Drug Interactions

  • Identification data

    Identifier: PC:680
    Authors:
    Sales-Pardo, M.Guimerà, R.
    Abstract:
    10.1371/journal.pcbi.1003374
  • Others:

    Author, as appears in the article.: Sales-Pardo, M. Guimerà, R.
    Department: Enginyeria Química
    Abstract: Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others. Here we present a network inference algorithm to predict uncharacterized drug-drug interactions. Our algorithm takes, as its only input, sets of previously reported interactions, and does not require any pharmacological or biochemical information about the drugs, their targets or their mechanisms of action. Because the models we use are abstract, our approach can deal with adverse interactions, synergistic/antagonistic/suppressing interactions, or any other type of drug interaction. We show that our method is able to accurately predict interactions, both in exhaustive pairwise interaction data between small sets of drugs, and in large-scale databases. We also demonstrate that our algorithm can be used efficiently to discover interactions of new drugs as part of the drug discovery process.
    Thematic Areas: Toxicity
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 1553-7358
    Journal volume: 9
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003374
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Article's DOI: 10.1371/journal.pcbi.1003374
    Entity: Universitat Rovira i Virgili.
    Journal publication year: 2013