Yize Zhao, PhD
Associate Professor of BiostatisticsCards
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Associate Professor of Biostatistics
Biography
Dr. Zhao is an Associate Professor in the Department of Biostatistics at Yale School of Public Health. She is also affiliated with Yale Center for Analytical Sciences, Yale Alzheimer's Disease Research Center and Yale Computational Biology and Bioinformatics. Her main research focuses on the development of statistical and machine learning methods to analyze large-scale complex data (imaging, -omics, EHRs), Bayesian methods, feature selection, predictive modeling, data integration, missing data and network analysis. She has strong interests in biomedical research areas including mental health, mental disorders and aging, etc. Her most recent research agenda includes analytical method development and applications on brain network analyses, multimodal imaging modeling, imaging genetics, and the integration of biomedical data with EHR data. Her research is supported by multiple NIH grants.
Dr. Zhao received her Ph.D. in Biostatistics from Emory University and postdoc training at Statistical and Applied Mathematical Sciences Institute (SAMSI) and the University of North Carolina at Chapel Hill. Prior to coming to Yale, she was an Assistant Professor in Biostatistics at Cornell University, Weill Cornell Medicine.
Appointments
Biostatistics
Associate Professor TenurePrimaryBiomedical Informatics & Data Science
Associate Professor on TermSecondary
Other Departments & Organizations
Education & Training
- PhD
- Emory University (2014)
- BS
- Zhejiang University (2010)
Research
Overview
Medical Subject Headings (MeSH)
Research at a Glance
Yale Co-Authors
Publications Timeline
Research Interests
Erich J Greene, PhD
Fan Li, PhD
Julian Zhao, MSPH
Can Meng, MS, MPH
Denise Esserman, PhD
Ondrej Blaha, PhD
Neuroimaging
Publications
2024
Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling
Wang S, Wang Y, Xu F, Shen L, Zhao Y, Initiative A. Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling. Medical Image Analysis 2024, 103309. DOI: 10.1016/j.media.2024.103309.Peer-Reviewed Original ResearchConceptsBrain structural connectivityBrain connectivityStructural connectivityBrain connectivity matricesDiffusion MRITopological propertiesGenerative network modelsWhite matter fiber tractsAttributes of nodesConnectivity estimatesBrain networksGroup-level connectivityConnectivity matrixBrain regionsConnectivity architectureKnowledge of nodesAlzheimer's diseaseLatent space modelAnatomical informationGroup-level effectsImprove biological interpretationExtensive simulationsNetwork modelABC modelKnowledge-guided learning methods for integrative analysis of multi-omics data
Li W, Ballard J, Zhao Y, Long Q. Knowledge-guided learning methods for integrative analysis of multi-omics data. Computational And Structural Biotechnology Journal 2024, 23: 1945-1950. PMID: 38736693, PMCID: PMC11087912, DOI: 10.1016/j.csbj.2024.04.053.Peer-Reviewed Original ResearchCitationsConceptsAnalysis of multi-omics dataIntegrative analysis of multi-omics dataMulti-omics dataMulti-omics data integration methodBiological knowledgeMulti-omics data integrationMulti-omics data analysisMulti-omics studiesIntegrated analysisFunctional genomicsFunctional proteomicsData integration methodsComplex diseasesMolecular mechanismsAlzheimer's diseaseComprehensive insightGenomeProteomicsGenesAnalytical challengesPathwayAlzheimerBayesian mixed model inference for genetic association under related samples with brain network phenotype
Tian X, Wang Y, Wang S, Zhao Y, Zhao Y. Bayesian mixed model inference for genetic association under related samples with brain network phenotype. Biostatistics 2024, kxae008. PMID: 38494649, DOI: 10.1093/biostatistics/kxae008.Peer-Reviewed Original ResearchConceptsSample relatednessGenetic studiesGenetic association studiesRisk genetic variantsImaging genetics studiesPopulation structureAssociation studiesQuantitative phenotypesQuantitative geneticsGenetic basisGenetic variantsGenetic associationGenetic contributionPhenotypeRelatednessConnectivity traitsNetwork phenotypesConnectivity phenotypesMarkov chain Monte CarloMixed-effects modelsPedigreeGeneticsBiological structuresTraitsHuman Connectome ProjectHeterogeneity Analysis on Multi-State Brain Functional Connectivity and Adolescent Neurocognition
Wang S, Constable T, Zhang H, Zhao Y. Heterogeneity Analysis on Multi-State Brain Functional Connectivity and Adolescent Neurocognition. Journal Of The American Statistical Association 2024, 119: 851-863. DOI: 10.1080/01621459.2024.2311363.Peer-Reviewed Original ResearchBayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event
Tian X, Ciarleglio M, Cai J, Greene E, Esserman D, Li F, Zhao Y. Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2024, 73: 598-620. PMID: 39072299, PMCID: PMC11271983, DOI: 10.1093/jrsssc/qlae003.Peer-Reviewed Original ResearchConceptsSemi-parametric inferenceRecurrent eventsAccelerated failure time modelFailure time modelEfficient sampling algorithmFrailty distributionDirichlet processPosterior inferenceSampling algorithmTime modelTerminal eventSurvival processesComplex data structuresDirichletInferenceData structureFall injury preventionAlgorithmHomological Landscape of Human Brain Functional Sub-Circuits
Duong-Tran D, Kaufmann R, Chen J, Wang X, Garai S, Xu F, Bao J, Amico E, Kaplan A, Petri G, Goni J, Zhao Y, Shen L. Homological Landscape of Human Brain Functional Sub-Circuits. Mathematics 2024, 12: 455. DOI: 10.3390/math12030455.Peer-Reviewed Original ResearchCitationsAltmetricConceptsFunctional sub-circuitsHuman brain functional connectivityWhole-brain functional connectivity networksNon-local propertiesWorking memory taskWhole-brain levelBrain functional connectivityNon-localFunctional connectivity networksMemory taskEmotional tasksLimbic networkMode networkFunctional connectivityBrain connectomeWhole-brainFormalismLocal structureMotor tasksConnectivity networksSubject domainSub-circuitsTask
2023
Identifying Shared Neuroanatomic Architecture Between Cognitive Traits Through Multiscale Morphometric Correlation Analysis
Wen Z, Bao J, Yang S, Risacher S, Saykin A, Thompson P, Davatzikos C, Huang H, Zhao Y, Shen L. Identifying Shared Neuroanatomic Architecture Between Cognitive Traits Through Multiscale Morphometric Correlation Analysis. Lecture Notes In Computer Science 2023, 14394: 227-240. PMID: 38584725, PMCID: PMC10993314, DOI: 10.1007/978-3-031-47425-5_21.Peer-Reviewed Original Research
2022
Simulating time-to-event data subject to competing risks and clustering: A review and synthesis
Meng C, Esserman D, Li F, Zhao Y, Blaha O, Lu W, Wang Y, Peduzzi P, Greene E. Simulating time-to-event data subject to competing risks and clustering: A review and synthesis. Statistical Methods In Medical Research 2022, 32: 305-333. PMID: 36412111, DOI: 10.1177/09622802221136067.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsSex-specific genetic association between psychiatric disorders and cognition, behavior and brain imaging in children and adults
Gui Y, Zhou X, Wang Z, Zhang Y, Wang Z, Zhou G, Zhao Y, Liu M, Lu H, Zhao H. Sex-specific genetic association between psychiatric disorders and cognition, behavior and brain imaging in children and adults. Translational Psychiatry 2022, 12: 347. PMID: 36028495, PMCID: PMC9418275, DOI: 10.1038/s41398-022-02041-6.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsCognitive functionFluid intelligenceCognitive traitsAdolescent Brain Cognitive Development (ABCD) studyPsychiatric disordersCognitive Development StudyMediation effectMost psychiatric disordersPolygenic risk scoresBrain functionBrain structuresBrain imagingEarly etiologySex differencesDevelopment studiesPsychiatric traitsChildrenIntelligenceDisordersSchizophreniaGenetic riskAdultsTraitsCognitionAutismBayesian network mediation analysis with application to the brain functional connectome
Zhao Y, Chen T, Cai J, Lichenstein S, Potenza M, Yip S. Bayesian network mediation analysis with application to the brain functional connectome. Statistics In Medicine 2022, 41: 3991-4005. PMID: 35795965, PMCID: PMC10131252, DOI: 10.1002/sim.9488.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsStochastic block modelBayesian paradigmBrain functional connectomeBlock modelConnectivity weightsFunctional connectomeNetwork measurementsEffect componentApproach applicationBlock allocationOpioid abstinenceAnalytic approachNetwork neurosciencePractical illustrationTherapeutic interventionsMediation analysisNeural circuitsNetwork structureBrain functioningMediatorsFunctional networksFeature selectionApplicationsModelNetwork
Academic Achievements & Community Involvement
activity Biometrics
Journal ServiceAssociate EditorDetails2022 - Presentactivity BMC Medical Research Methodology
Journal ServiceAssociate EditorDetails2019 - Presentactivity Regional Advisory Board
Advisory BoardsMemberDetailsInternational Biometric Society/Eastern North American Region2019 - 2022honor Elected member
International AwardInternational Statistical InstituteDetails07/01/2021Netherlandshonor Yale Alzheimer's Disease Research Center Research Scholar Award
Yale School of Medicine AwardYale Alzheimer's Disease Research CenterDetails06/01/2021United States
News & Links
News
- August 29, 2024
Yale Researchers Awarded $20.6M Grant for Wide-Ranging Study of Mental Illness
- May 23, 2024
2024 Top Research Awards Announced
- January 10, 2023
Award-winning Associate Professor Yize Zhao Applies Innovative Statistical Methods to Advance Medical Science
- February 10, 2022
YSPH grant awards top $60 million in fiscal 2021
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