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

ABSTRACT:

Objective

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).

Materials and methods

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 >30 different resources, including >10 000 drugs and >1.7 million drug-gene pairs. We appl

SUBMITTER: Patrick MT 

PROVIDER: S-EPMC8200269 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature