Articles producció científica> Enginyeria Química

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

  • Datos identificativos

    Identificador: PC:680
  • Autores:

    Sales-Pardo, M.
    Guimerà, R.
  • Otros:

    Autor según el artículo: Sales-Pardo, M. Guimerà, R.
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original:
    Departamento: Enginyeria Química
    DOI del artículo: 10.1371/journal.pcbi.1003374
    Resumen: 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.
    Entidad: Universitat Rovira i Virgili.
    Áreas temáticas: Toxicity
    Año de publicación de la revista: 2013
    Acceso a la licencia de uso:
    ISSN: 1553-7358
    Volumen de revista: 9