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Patrick, Matthew T; Bardhi, Redina; Raja, Kalpana; He, Kevin; Tsoi, Lam C

Advancement in predicting interactions between drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources

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  • Medientyp: E-Artikel
  • Titel: Advancement in predicting interactions between drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources
  • Beteiligte: Patrick, Matthew T; Bardhi, Redina; Raja, Kalpana; He, Kevin; Tsoi, Lam C
  • Erschienen: Oxford University Press (OUP), 2021
  • Erschienen in: Journal of the American Medical Informatics Association
  • Sprache: Englisch
  • DOI: 10.1093/jamia/ocaa335
  • ISSN: 1527-974X
  • Schlagwörter: Health Informatics
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Objective</jats:title> <jats:p>Drug–drug interactions (DDIs) can result in adverse and potentially life-threatening health consequences; however, it is challenging to predict potential DDIs in advance. We introduce a new computational approach to comprehensively assess the drug pairs which may be involved in specific DDI types by combining information from large-scale gene expression (984 transcriptomic datasets), molecular structure (2159 drugs), and medical claims (150 million patients).</jats:p> </jats:sec> <jats:sec> <jats:title>Materials and Methods</jats:title> <jats:p>Features were integrated using ensemble machine learning techniques, and we evaluated the DDIs predicted with a large hospital-based medical records dataset. Our pipeline integrates information from &amp;gt;30 different resources, including &amp;gt;10 000 drugs and &amp;gt;1.7 million drug–gene pairs. We applied our technique to predict interactions between 37 611 drug pairs used to treat psoriasis and its comorbidities.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>Our approach achieves &amp;gt;0.9 area under the receiver operator curve (AUROC) for differentiating 11 861 known DDIs from 25 750 non-DDI drug pairs. Significantly, we demonstrate that the novel DDIs we predict can be confirmed through independent data sources and supported using clinical medical records.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>By applying machine learning and taking advantage of molecular, genomic, and health record data, we are able to accurately predict potential new DDIs that can have an impact on public health.</jats:p> </jats:sec>
  • Zugangsstatus: Freier Zugang