Qingzhou Feng
Postdoctoral Associate in Cell BiologyAbout
Research
Publications
2025
Improving topic modeling performance on social media through semantic relationships within biomedical terminology
Xin Y, Grabowska M, Gangireddy S, Krantz M, Kerchberger V, Dickson A, Feng Q, Yin Z, Wei W. Improving topic modeling performance on social media through semantic relationships within biomedical terminology. PLOS ONE 2025, 20: e0318702. PMID: 39982945, PMCID: PMC11845042, DOI: 10.1371/journal.pone.0318702.Peer-Reviewed Original ResearchConceptsSocial media textsTopic modelsSocial mediaHealth-related topicsAnalyze social mediaSemantic relationshipsBiomedical terminologiesMedical conceptsSemantic typesRecord validationModeling pipelineMedia textsUnsupervised machineExpert evaluationHealthcare researchModel performanceOnline discussionsTextTopicsPipelineUsersMachineModeling approachModelTechnique's potentialRisk factors affecting polygenic score performance across diverse cohorts
Hui D, Dudek S, Kiryluk K, Walunas T, Kullo I, Wei W, Tiwari H, Peterson J, Chung W, Davis B, Khan A, Kottyan L, Limdi N, Feng Q, Puckelwartz M, Weng C, Smith J, Karlson E, Center R, BioBank P, Jarvik G, Ritchie M. Risk factors affecting polygenic score performance across diverse cohorts. ELife 2025, 12: rp88149. PMID: 39851248, PMCID: PMC11771958, DOI: 10.7554/elife.88149.Peer-Reviewed Original ResearchConceptsBody mass indexPolygenic scoresAssociated with body mass indexPolygenic score performancePhysical activityStandard deviation changeAlcohol consumptionMass indexDiverse cohortInteraction effectsRisk factorsBlood lipidsDeviation changeQuintileScore performanceCohortCovariatesBinary covariateAncestryModel R<sup>2</sup>Continuous covariatesDifferencesRisk factors affecting polygenic score performance across diverse cohorts
Hui D, Dudek S, Kiryluk K, Walunas T, Kullo I, Wei W, Tiwari H, Peterson J, Chung W, Davis B, Khan A, Kottyan L, Limdi N, Feng Q, Puckelwartz M, Weng C, Smith J, Karlson E, Jarvik G, Ritchie M. Risk factors affecting polygenic score performance across diverse cohorts. ELife 2025, 12 DOI: 10.7554/elife.88149.3.Peer-Reviewed Original ResearchBody mass indexPolygenic scoresAssociated with body mass indexPolygenic score performancePhysical activityStandard deviation changeBMI effectsBMI individualsAlcohol consumptionDiverse cohortMass indexInteraction effectsRisk factorsBlood lipidsDeviation changeQuintileScore performanceCohortCovariatesBinary covariateAncestryContinuous covariatesModel R 2DifferencesScores
2024
Pharmacogenomics polygenic risk score: Ready or not for prime time?
Singh S, Stocco G, Theken K, Dickson A, Feng Q, Karnes J, Mosley J, Rouby N. Pharmacogenomics polygenic risk score: Ready or not for prime time? Clinical And Translational Science 2024, 17: e13893. PMID: 39078255, PMCID: PMC11287822, DOI: 10.1111/cts.13893.Peer-Reviewed Original ResearchConceptsGenome wide association studiesDisease-associated variantsPharmacogenetic phenotypesPharmacogenomic researchPolygenic risk scoresDisease geneticsGenomic dataAssociation studiesPhenotypic inconsistenciesPolygenic natureGenetic variantsGenetic associationStratified treatment approachDrug responseClinical decision toolPrecision medicineClinical predictorsPhenotypeClinical useClinical implementationTreatment approachesRisk scoreVariantsGenomePatient educationAB1426 RAB19 AND PANCREATIC INJURY IN PATIENTS TAKING AZATHIOPRINE: A GENOME-WIDE ASSOCIATION STUDY
Shah S, Reese T, Daniel L, Nepal P, Zanussi J, Dickson A, Tao R, Hung A, Wei W, Stein C, Feng Q, Chung C. AB1426 RAB19 AND PANCREATIC INJURY IN PATIENTS TAKING AZATHIOPRINE: A GENOME-WIDE ASSOCIATION STUDY. Annals Of The Rheumatic Diseases 2024, 83: 2069. DOI: 10.1136/annrheumdis-2024-eular.1246.Peer-Reviewed Original ResearchCharacterizing genetic profiles for high triglyceride levels in U.S. patients of African ancestry
Jiang L, Gangireddy S, Dickson A, Xin Y, Yan C, Kawai V, Cox N, Linton M, Wei W, Stein C, Feng Q. Characterizing genetic profiles for high triglyceride levels in U.S. patients of African ancestry. Journal Of Lipid Research 2024, 65: 100569. PMID: 38795861, PMCID: PMC11231545, DOI: 10.1016/j.jlr.2024.100569.Peer-Reviewed Original ResearchIndividuals of AAElectronic health recordsMild-to-moderate HTGGenetic risk factorsAfrican ancestryEuropean ancestryGenetic profileIndividuals of European ancestryRisk factorsNormal TGLongitudinal electronic health recordsSevere hypertriglyceridemiaPolygenic risk scoresCohort of AA patientsPatients of African ancestryAPOA5 p.Metabolic genesFunctional variantsCardiovascular risk factorsHigher triglyceride levelsHealth recordsVariant allelesAncestryRisk scoreAA patientsGlucagon-Like Peptide-1 Receptor Agonists Are Associated With Reduced Sepsis and Organ Injury in Adults Hospitalized With Infection
Aman L, Obeidalla S, Bastarache J, Baer B, Wei W, Feng Q, Kerchberger V. Glucagon-Like Peptide-1 Receptor Agonists Are Associated With Reduced Sepsis and Organ Injury in Adults Hospitalized With Infection. 2024, a1108-a1108. DOI: 10.1164/ajrccm-conference.2024.209.1_meetingabstracts.a1108.Peer-Reviewed Original ResearchLarge language models facilitate the generation of electronic health record phenotyping algorithms
Yan C, Ong H, Grabowska M, Krantz M, Su W, Dickson A, Peterson J, Feng Q, Roden D, Stein C, Kerchberger V, Malin B, Wei W. Large language models facilitate the generation of electronic health record phenotyping algorithms. Journal Of The American Medical Informatics Association 2024, 31: 1994-2001. PMID: 38613820, PMCID: PMC11339509, DOI: 10.1093/jamia/ocae072.Peer-Reviewed Original ResearchPhenotyping algorithmsLanguage modelElectronic health recordsHigh-quality algorithmsElectronic health record phenotyping algorithmsSQL executionSQL queriesDomain expertsKnowledge discoveryLimited scalabilityData modelCritical metricsAlgorithmic logicAlgorithmCore taskAccurate algorithmHealth recordsSQLElectronic medical recordsExpert evaluation scoresHealth researchEvidence synthesisEHR phenotypesLogicChatGPTIndividualized Dose-Response to Statins Associated With Cardiovascular Disease Outcomes
Aggarwal S, Jiang L, Liu G, Grabowska M, Ong H, Wilke R, Feng Q, Wei W. Individualized Dose-Response to Statins Associated With Cardiovascular Disease Outcomes. JACC Advances 2024, 3: 100894. PMID: 38737008, PMCID: PMC11086740, DOI: 10.1016/j.jacadv.2024.100894.Peer-Reviewed Original ResearchAtherosclerotic cardiovascular diseaseAtherosclerotic cardiovascular disease eventsLDL-CComposite primary end pointPrevention of atherosclerotic cardiovascular diseaseDose responseLow-density lipoprotein cholesterolBaseline LDL-CPrimary end pointAll-cause mortalityElectronic health record dataTime-to-event analysisHealth record dataCardiovascular disease outcomesIndividual dose responsesMultiple dosesClinical outcomesPrescribed statinsLipoprotein cholesterolHazard ratioStatinsEnd pointsDisease outcomeCardiovascular diseaseED50Leveraging generative AI to prioritize drug repurposing candidates for Alzheimer’s disease with real-world clinical validation
Yan C, Grabowska M, Dickson A, Li B, Wen Z, Roden D, Michael Stein C, Embí P, Peterson J, Feng Q, Malin B, Wei W. Leveraging generative AI to prioritize drug repurposing candidates for Alzheimer’s disease with real-world clinical validation. Npj Digital Medicine 2024, 7: 46. PMID: 38409350, PMCID: PMC10897392, DOI: 10.1038/s41746-024-01038-3.Peer-Reviewed Original ResearchGenerative artificial intelligenceDrug repurposing candidatesAlzheimer's diseaseRepurposing candidatesSearch spaceGenerative AIArtificial intelligenceChatGPTAD riskClinical datasetsAssociated with lower AD riskLower AD riskDrug repurposingDrug developmentAlzheimerTreatment of diseasesTechnologyDatasetIntelligence