2024
SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials
Lee K, Paek H, Huang L, Hilton C, Datta S, Higashi J, Ofoegbu N, Wang J, Rubinstein S, Cowan A, Kwok M, Warner J, Xu H, Wang X. SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials. Informatics In Medicine Unlocked 2024, 50: 101589. PMID: 39493413, PMCID: PMC11530223, DOI: 10.1016/j.imu.2024.101589.Peer-Reviewed Original ResearchAntibody-drug conjugatesOverall response rateMultiple myelomaF1 scoreCAR-TComplete responseBispecific antibodiesComparative performance analysisClinical trial studyClinical trial outcomesLanguage modelAccurate data extractionTherapy subgroupFine granularityOncology clinical trialsAdverse eventsClinical decision-makingPerformance analysisClinical trialsInnovative therapiesDiverse therapiesClinical trial abstractsCancer domainData elementsTherapy
2017
A systematic analysis of FDA-approved anticancer drugs
Sun J, Wei Q, Zhou Y, Wang J, Liu Q, Xu H. A systematic analysis of FDA-approved anticancer drugs. BMC Systems Biology 2017, 11: 87. PMID: 28984210, PMCID: PMC5629554, DOI: 10.1186/s12918-017-0464-7.Peer-Reviewed Original ResearchConceptsDrug-cancer associationsAnticancer drugsTarget-based drugsEfficient anticancer drugsTarget-based approachCancer typesNew anticancer drugsNovel anticancer drugsClinical trial studyPharmaceutical researchTrial studyMore cancer typesUS FoodDrug AdministrationCytotoxic drugsPatient treatmentPotential candidateDrug mechanismsDrugsDrug repurposingSystematic investigationAssociationDrug targetsTyrosine kinaseSystematic discovery