Mediation analysis in longitudinal study with high-dimensional methylation mediators
Cui Y, Lin Q, Yuan X, Jiang F, Ma S, Yu Z. Mediation analysis in longitudinal study with high-dimensional methylation mediators. Briefings In Bioinformatics 2024, 25: bbae496. PMID: 39406521, PMCID: PMC11479716, DOI: 10.1093/bib/bbae496.Peer-Reviewed Original ResearchConceptsBody mass indexMediation analysisPaternal body mass indexGeneralized estimating equationsLinear mixed-effects modelsCohort dataMass indexMixed-effects modelsDNA methylation sitesHigh-dimensional mediation analysisLongitudinal studyBonferroni correctionAccurate parameter estimatesSobel testVariable selectionLongitudinal dataSimulation studyIndependence screeningMethylation sitesCpG sitesIndirect effectsParameter estimationLinearization methodHypothesis testingHEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics
Yuan X, Ma Y, Gao R, Cui S, Wang Y, Fa B, Ma S, Wei T, Ma S, Yu Z. HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics. Nature Communications 2024, 15: 5700. PMID: 38972896, PMCID: PMC11228050, DOI: 10.1038/s41467-024-49846-1.Peer-Reviewed Original ResearchConceptsSpatially variable genesVariable genesSpatial expression patternsSpatial transcriptomics technologiesSpatial transcriptomics researchTranscriptome researchTranscriptomic technologiesBiological functionsExpression patternsSpatial transcriptomicsGenesState-of-the-art methodsColorectal cancer dataSingle-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression
Gong Y, Xu J, Wu M, Gao R, Sun J, Yu Z, Zhang Y. Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression. Cell Reports Methods 2024, 4: 100742. PMID: 38554701, PMCID: PMC11045878, DOI: 10.1016/j.crmeth.2024.100742.Peer-Reviewed Original ResearchConceptsSnRNA-seq dataGene modulesAD progressionPathogenesis of Alzheimer's diseaseBiologically interpretable resultsSingle-cell data analysisGene regulatory changesFunctional gene modulesGene coexpression patternsAlzheimer's diseaseSingle-cell levelSnRNA-seqBiclustering methodsPolygenic diseaseBatch effectsDropout eventsCoexpression patternsNetwork biomarkersCell typesBiclusteringCellsGenesScRNABiologyComparative analysisPredicting long-term progression of Alzheimer’s disease using a multimodal deep learning model incorporating interaction effects
Wang Y, Gao R, Wei T, Johnston L, Yuan X, Zhang Y, Yu Z. Predicting long-term progression of Alzheimer’s disease using a multimodal deep learning model incorporating interaction effects. Journal Of Translational Medicine 2024, 22: 265. PMID: 38468358, PMCID: PMC10926590, DOI: 10.1186/s12967-024-05025-w.Peer-Reviewed Original ResearchConceptsAlzheimer's diseaseGenetic polymorphism dataProgression of Alzheimer's diseaseMild cognitive impairmentPolymorphism dataAlzheimer's Disease Neuroimaging InitiativeAD progressionArea under the receiver operating characteristic curvePrediction of AD progressionDeep learning modelsADNI-1AlzheimerPatient careLearning modelsMCI to ADInteraction effectsADNI-3Increase prediction accuracyMild cognitive impairment to ADEarly interventionCognitive impairmentClinical assessmentA generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints
Chi X, Yuan Y, Yu Z, Lin R. A generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints. Biometrical Journal 2024, 66: e2300122. PMID: 38368277, PMCID: PMC11323483, DOI: 10.1002/bimj.202300122.Peer-Reviewed Original ResearchConceptsBayesian hierarchical modeling approachShrinkage parameterTheoretical propertiesPhase II basket trialBasket trialsSimulation studyHierarchical modelHierarchical modeling approachFunctional formGeneral hierarchical modelEfficacy endpointLatent variable approachApproach yieldsRisk-benefit profileVariable approachImmunotherapy agentsTumor responseGeneralizationTargeted therapyCancer subtypesMultiple cancer subtypesTreatment effectsMonitoring proceduresTypes of On-Screen Content and Mental Health in Kindergarten Children
Wang H, Zhao J, Yu Z, Pan H, Wu S, Zhu Q, Dong Y, Liu H, Zhang Y, Jiang F. Types of On-Screen Content and Mental Health in Kindergarten Children. JAMA Pediatrics 2024, 178: 125-132. PMID: 38048076, PMCID: PMC10696513, DOI: 10.1001/jamapediatrics.2023.5220.Peer-Reviewed Original ResearchConceptsMental health problemsAged 3 to 6 yearsExposure to educational programsAssociated with mental health problemsChildren aged 3 to 6 yearsMental healthHealth problemsScreen timeEducation programsScreen exposureMental health of childrenExcessive screen timeChildren's screen timeStrengths and Difficulties QuestionnaireShanghai Children's HealthHealth of childrenHigh riskAged 3Aged 3 to 4 yearsChildren's HealthMain OutcomesDifficulties QuestionnaireCohort studyWave 1Health