2024
Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition
Zuo X, Kumar A, Shen S, Li J, Cong G, Jin E, Chen Q, Warner J, Yang P, Xu H. Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition. JCO Clinical Cancer Informatics 2024, 8: e2300166. PMID: 38885475, DOI: 10.1200/cci.23.00166.Peer-Reviewed Original ResearchConceptsNatural language processingDomain-specific language modelsNatural language processing systemsInformation extraction systemRule-based moduleNarrative clinical textsNLP tasksEntity recognitionText normalizationAssertion classificationLanguage modelInformation extractionClinical textElectronic health recordsLearning-basedClinical notesLanguage processingTest setSystem performanceHealth recordsResponse extractionTime-consumingAnticancer therapyInformationAssessment information
2022
Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing
Wang L, Fu S, Wen A, Ruan X, He H, Liu S, Moon S, Mai M, Riaz I, Wang N, Yang P, Xu H, Warner J, Liu H. Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing. JCO Clinical Cancer Informatics 2022, 6: e2200006. PMID: 35917480, PMCID: PMC9470142, DOI: 10.1200/cci.22.00006.Peer-Reviewed Original Research
2021
OncoSplicing: an updated database for clinically relevant alternative splicing in 33 human cancers
Zhang Y, Yao X, Zhou H, Wu X, Tian J, Zeng J, Yan L, Duan C, Liu H, Li H, Chen K, Hu Z, Ye Z, Xu H. OncoSplicing: an updated database for clinically relevant alternative splicing in 33 human cancers. Nucleic Acids Research 2021, 50: d1340-d1347. PMID: 34554251, PMCID: PMC8728274, DOI: 10.1093/nar/gkab851.Peer-Reviewed Original ResearchConceptsAlternative splicingCancer-specific splicing eventsDifferential alternative splicingHuman cancersTCGA tumor samplesSplicing differencesSplicing eventsProtein complexityAdjacent normal samplesSplicingGene expressionSplicing dataNormal samplesAbnormal splicingIntegrative viewMRNA levelsDifferential analysisTumor samplesTranscriptsPresence of complete murine viral genome sequences in patient-derived xenografts
Yuan Z, Fan X, Zhu J, Fu T, Wu J, Xu H, Zhang N, An Z, Zheng W. Presence of complete murine viral genome sequences in patient-derived xenografts. Nature Communications 2021, 12: 2031. PMID: 33795676, PMCID: PMC8017013, DOI: 10.1038/s41467-021-22200-5.Peer-Reviewed Original ResearchConceptsPatient-derived xenograftsViral infectionMurine viral infectionHigh virus loadDrug developmentDrug metabolism-related genesVirus loadXenograft experimentsMetabolism-related genesXenograftsUnbiased data-driven approachTumor cellsInfectionExpression levelsEntire viral genomeViral genome sequencesViral sequencesViral genomeCancerImmune
2020
Efficient and Accurate Extracting of Unstructured EHRs on Cancer Therapy Responses for the Development of RECIST Natural Language Processing Tools: Part I, the Corpus
Li Y, Luo Y, Wampfler J, Rubinstein S, Tiryaki F, Ashok K, Warner J, Xu H, Yang P. Efficient and Accurate Extracting of Unstructured EHRs on Cancer Therapy Responses for the Development of RECIST Natural Language Processing Tools: Part I, the Corpus. JCO Clinical Cancer Informatics 2020, 4: cci.19.00147. PMID: 32364754, PMCID: PMC7265793, DOI: 10.1200/cci.19.00147.Peer-Reviewed Original ResearchConceptsNatural language processing toolsElectronic health recordsLanguage processing toolsGold standard dataUnstructured electronic health recordsProcessing toolsAmount of dataClinical notesStandard dataMayo Clinic electronic health recordsClinic's electronic health recordEnvironment toolsAccurate annotationHealth recordsInformatics toolsEffective analysisData setsTextual sourcesCorpusToolInformationData extractionSetExtractingAnnotationAchievability to Extract Specific Date Information for Cancer Research.
Wang L, Wampfler J, Dispenzieri A, Xu H, Yang P, Liu H. Achievability to Extract Specific Date Information for Cancer Research. AMIA Annual Symposium Proceedings 2020, 2019: 893-902. PMID: 32308886, PMCID: PMC7153063.Peer-Reviewed Original Research
2019
Developing Customizable Cancer Information Extraction Modules for Pathology Reports Using CLAMP
Soysal E, Warner J, Wang J, Jiang M, Harvey K, Jain S, Dong X, Song H, Siddhanamatha H, Wang L, Dai Q, Chen Q, Du X, Tao C, Yang P, Denny J, Liu H, Xu H. Developing Customizable Cancer Information Extraction Modules for Pathology Reports Using CLAMP. 2019, 264: 1041-1045. PMID: 31438083, PMCID: PMC7359882, DOI: 10.3233/shti190383.Peer-Reviewed Original ResearchConceptsElectronic health recordsNLP solutionNatural language processing technologyInformation extraction moduleLanguage processing technologyInformation extraction tasksUser-friendly interfaceBest F-measureInformation extractionExtraction moduleExtraction taskCustomizable modulesNLP systemsF-measureAcademic useHealth recordsComparable performanceProcessing technologyVanderbilt University Medical CenterModuleDiverse typesInformationNLPSubstantial effortSystemDiscovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing
Wu Y, Warner J, Wang L, Jiang M, Xu J, Chen Q, Nian H, Dai Q, Du X, Yang P, Denny J, Liu H, Xu H. Discovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing. JCO Clinical Cancer Informatics 2019, 3: cci.19.00001. PMID: 31141421, PMCID: PMC6693869, DOI: 10.1200/cci.19.00001.Peer-Reviewed Original ResearchConceptsVanderbilt University Medical CenterCancer survivalMayo ClinicDrug repurposingNoncancer drugsElectronic health record dataCancer registry dataEHR dataClinical trial evaluationOverall cancer survivalUniversity Medical CenterHealth record dataElectronic health recordsTreatment of cancerClinical trialsDrug classesRegistry dataMedical CenterDrug effectsSignificant associationLongitudinal EHRNew indicationsPatientsCancerHealth records
2018
Predict effective drug combination by deep belief network and ontology fingerprints
Chen G, Tsoi A, Xu H, Zheng W. Predict effective drug combination by deep belief network and ontology fingerprints. Journal Of Biomedical Informatics 2018, 85: 149-154. PMID: 30081101, DOI: 10.1016/j.jbi.2018.07.024.Peer-Reviewed Original Research
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 discoveryLightweight predicate extraction for patient-level cancer information and ontology development
Amith M, Song H, Zhang Y, Xu H, Tao C. Lightweight predicate extraction for patient-level cancer information and ontology development. BMC Medical Informatics And Decision Making 2017, 17: 73. PMID: 28699547, PMCID: PMC5506564, DOI: 10.1186/s12911-017-0465-x.Peer-Reviewed Original ResearchMeSH KeywordsBiological OntologiesHumansMedlinePlusNatural Language ProcessingNeoplasmsPublic HealthConceptsOntological knowledgebaseKnowledge triplesInformation extraction toolsDevelopment of ontologiesNatural language domainRDF representationSoftware libraryOntology developmentCustom applicationsOntologyDevelopment processExtraction toolAccurate extractionPublic health domainKnowledgebaseTextual sourcesTriplesKnowledgebasesHealth domainsToolExtractionTaskMethodsThis paperMedlinePlusDomainCATTLE (CAncer treatment treasury with linked evidence): An integrated knowledge base for personalized oncology research and practice
Soysal E, Lee H, Zhang Y, Huang L, Chen X, Wei Q, Zheng W, Chang J, Cohen T, Sun J, Xu H. CATTLE (CAncer treatment treasury with linked evidence): An integrated knowledge base for personalized oncology research and practice. CPT Pharmacometrics & Systems Pharmacology 2017, 6: 188-196. PMID: 28296354, PMCID: PMC5351410, DOI: 10.1002/psp4.12174.Peer-Reviewed Original ResearchComparing Cancer Information Needs for Consumers in the US and China.
Ji Z, Zhang Y, Xu J, Chen X, Wu Y, Xu H. Comparing Cancer Information Needs for Consumers in the US and China. 2017, 245: 126-130. PMID: 29295066, PMCID: PMC5805146.Peer-Reviewed Original Research
2016
Automated identification of molecular effects of drugs (AIMED)
Fathiamini S, Johnson A, Zeng J, Araya A, Holla V, Bailey A, Litzenburger B, Sanchez N, Khotskaya Y, Xu H, Meric-Bernstam F, Bernstam E, Cohen T. Automated identification of molecular effects of drugs (AIMED). Journal Of The American Medical Informatics Association 2016, 23: 758-765. PMID: 27107438, PMCID: PMC4926748, DOI: 10.1093/jamia/ocw030.Peer-Reviewed Original ResearchExtracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov
Xu J, Lee H, Zeng J, Wu Y, Zhang Y, Huang L, Johnson A, Holla V, Bailey A, Cohen T, Meric-Bernstam F, Bernstam E, Xu H. Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov. Journal Of The American Medical Informatics Association 2016, 23: 750-757. PMID: 27013523, PMCID: PMC4926744, DOI: 10.1093/jamia/ocw009.Peer-Reviewed Original ResearchToward Repurposing Metformin as a Precision Anti-Cancer Therapy Using Structural Systems Pharmacology
Hart T, Dider S, Han W, Xu H, Zhao Z, Xie L. Toward Repurposing Metformin as a Precision Anti-Cancer Therapy Using Structural Systems Pharmacology. Scientific Reports 2016, 6: 20441. PMID: 26841718, PMCID: PMC4740793, DOI: 10.1038/srep20441.Peer-Reviewed Original ResearchConceptsPrecision anti-cancer therapyMolecular basisAnti-cancer therapyStructural systems pharmacologyProtein-protein interactionsDrug target identificationNetwork biology analysisMolecular targetsInteractomic dataGenetic interactionsStructural proteomeGenetic networksKey molecular targetsPhenotypic responsesKinase targetsBiology analysisCancer mutationsPleiotropic effectsAnti-cancer effectsNetwork biomarkersTarget identificationGenetic biomarkersSystems pharmacology approachKey nodesTarget
2015
Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action
Sun J, Zhao M, Jia P, Wang L, Wu Y, Iverson C, Zhou Y, Bowton E, Roden D, Denny J, Aldrich M, Xu H, Zhao Z. Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action. PLOS Computational Biology 2015, 11: e1004202. PMID: 26083494, PMCID: PMC4470683, DOI: 10.1371/journal.pcbi.1004202.Peer-Reviewed Original ResearchConceptsGWAS datasetsPathway networkDisease genesGenome-wide association study datasetDrug targetsSignal transduction networksSignal transduction cascadeMultiple signaling pathwaysDrug-induced gene expressionNovel drug targetsTransduction networksTransduction cascadeEnrichment analysisGene expressionCommon genesMolecular mechanismsSignaling pathwaysGenesNovel MycLiterature miningMolecular modePathwayMetformin actionDrug actionDisease pathogenesisOncogenes and tumor suppressor genes: comparative genomics and network perspectives
Zhu K, Liu Q, Zhou Y, Tao C, Zhao Z, Sun J, Xu H. Oncogenes and tumor suppressor genes: comparative genomics and network perspectives. BMC Genomics 2015, 16: s8. PMID: 26099335, PMCID: PMC4474543, DOI: 10.1186/1471-2164-16-s7-s8.Peer-Reviewed Original ResearchConceptsTumor suppressor geneEssential genesTSG proteinsEssential proteinsHuman protein-protein interaction networkCancer drug target proteinsSuppressor geneWhole human interactomeProtein-protein interaction networkCancer developmentMutation frequencyDirect interactionDrug target identificationCancer drug targetSomatic mutationsDrug target proteinsDrug target genesComparative genomicsLow mutation frequencyCellular functionsHuman interactomeMutation patternsGenetic variationGene setsHigh mutation frequency
2014
Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality
Xu H, Aldrich M, Chen Q, Liu H, Peterson N, Dai Q, Levy M, Shah A, Han X, Ruan X, Jiang M, Li Y, St Julien J, Warner J, Friedman C, Roden D, Denny J. Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. Journal Of The American Medical Informatics Association 2014, 22: 179-191. PMID: 25053577, PMCID: PMC4433365, DOI: 10.1136/amiajnl-2014-002649.Peer-Reviewed Original ResearchConceptsType 2 diabetes patientsElectronic health recordsCancer patientsCancer mortalityDiabetes patientsEHR dataNon-diabetic cancer patientsCox proportional hazards modelDrug exposure informationOral hypoglycemic medicationsCharlson Comorbidity IndexNon-diabetic patientsUse of metforminCancer diagnosisHealth recordsSite-specific cancersBody mass indexProportional hazards modelVanderbilt University Medical CenterUniversity Medical CenterLarge electronic health recordHypoglycemic medicationsCause mortalityComorbidity indexInsulin use
2012
Identifying the status of genetic lesions in cancer clinical trial documents using machine learning
Wu Y, Levy M, Micheel C, Yeh P, Tang B, Cantrell M, Cooreman S, Xu H. Identifying the status of genetic lesions in cancer clinical trial documents using machine learning. BMC Genomics 2012, 13: s21. PMID: 23282337, PMCID: PMC3535695, DOI: 10.1186/1471-2164-13-s8-s21.Peer-Reviewed Original Research