{"doi":"10.1093/jamia/ocaa335","title":"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 applied our technique to predict interactions between 37 611 drug pairs used to treat psoriasis and its comorbidities. RESULTS: Our approach achieves >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. CONCLUSIONS: 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.","journal":"Journal of the American Medical Informatics Association","year":2020,"id":82444,"datarank":0.3958585994422889,"base_score":2.639057329615259,"endowment":2.639057329615259,"self_citation_contribution":0.3958585994422889,"citation_network_contribution":0.0,"self_endowment_contribution":0.3958585994422889,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9471,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":427005,"name":"Redina Bardhi","orcid":null,"position":1,"is_corresponding":false},{"id":425611,"name":"Kalpana Raja","orcid":"0000-0002-3156-4197","position":2,"is_corresponding":false},{"id":96843,"name":"Kevin He","orcid":"0000-0002-8354-426X","position":3,"is_corresponding":false},{"id":12819,"name":"Lam C. Tsoi","orcid":"0000-0003-1627-5722","position":4,"is_corresponding":false},{"id":249271,"name":"Matthew T. Patrick","orcid":"0000-0002-6174-9002","position":0,"is_corresponding":true}],"reference_count":66,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T21:53:48.608934Z","pmid":"33544847","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}