2025
GE-IA-NAM: gene–environment interaction analysis via imaging-assisted neural additive model
Li J, Xu Y, Ma S, Fang K. GE-IA-NAM: gene–environment interaction analysis via imaging-assisted neural additive model. Bioinformatics 2025, 41: btaf481. PMID: 40880282, PMCID: PMC12452269, DOI: 10.1093/bioinformatics/btaf481.Peer-Reviewed Original ResearchConceptsGene-environmentNeural additive modelsGene-environment modelGene-environment analysisGene-environment interaction analysisEnvironmental factorsCancer Genome AtlasPathological imagesSkin cancer datasetGenome AtlasCancer datasetsNetwork architectureCompetitive performanceGenetic factorsPython codeCancer outcomesInteraction analysisData patternsCancer researchAdditive modelInteraction methodEnvironmental dataJoint analysisCancer modelsRegression-basedSubgroup Testing in the Change‐Plane Cox Model
Zhang X, Ren P, Shi X, Ma S, Liu X. Subgroup Testing in the Change‐Plane Cox Model. Statistics In Medicine 2025, 44: e70179. PMID: 40662752, DOI: 10.1002/sim.70179.Peer-Reviewed Original ResearchConceptsFinite sample performanceAnalysis of survival dataLikelihood ratio testAsymptotic distributionSample performanceLung cancer dataScore testSimulation studyRatio testSurvival dataCancer dataCox modelImmune checkpoint blockade therapyCheckpoint blockade therapySolid tumor patientsTumor mutational burdenSubgroup testsTreatment effectsCovariatesBlockade therapyMutational burdenSubgroupsRobust Transfer Learning for High‐Dimensional GLM Using γ$$ \gamma $$‐Divergence With Applications to Cancer Genomics
Xu F, Ma S, Zhang Q, Xu Y. Robust Transfer Learning for High‐Dimensional GLM Using γ$$ \gamma $$‐Divergence With Applications to Cancer Genomics. Statistics In Medicine 2025, 44: e70170. PMID: 40662636, PMCID: PMC12313224, DOI: 10.1002/sim.70170.Peer-Reviewed Original ResearchConceptsTransfer learningReal world biomedical dataRisk of negative transferProximal gradient descentTransfer learning methodTransfer learning approachHigh-dimensional dataHigh-dimensional settingsGradient descentCompetitive performanceLearning methodsEstimation error boundsBiomedical dataEfficient algorithmLearning approachDetection schemeNegative transferAnalysis of complex diseasesDebiasing stepMethod's effectivenessCancer genomic dataData contaminationError boundsHigh-dimensional profiling dataOutliersA Selective Review of Network Analysis Methods for Gene Expression Data
Li R, Yi H, Ma S. A Selective Review of Network Analysis Methods for Gene Expression Data. Methods In Molecular Biology 2025, 2880: 293-307. PMID: 39900765, DOI: 10.1007/978-1-0716-4276-4_14.Peer-Reviewed Original ResearchConceptsGene expression dataGene expression networksExpression dataDownstream analysisExpression networksGene expressionBiological processesGenesMolecular mechanismsBiological implicationsHigh-throughput profiling techniquesBiological findingsGlobal viewComplex interactionsProfiling techniquesRegulationHierarchical Multi‐Label Classification With Gene‐Environment Interactions in Disease Modeling
Li J, Zhang Q, Ma S, Fang K, Xu Y. Hierarchical Multi‐Label Classification With Gene‐Environment Interactions in Disease Modeling. Statistics In Medicine 2025, 44: e10330. PMID: 39865593, PMCID: PMC12201914, DOI: 10.1002/sim.10330.Peer-Reviewed Original ResearchConceptsHierarchical multi-label classificationMulti-label classificationGene-environment interaction analysisGene-environmentEfficient expectation-maximizationGene-environment interactionsSemi-supervised scenariosCancer Genome AtlasUnlabeled dataInteraction analysisExpectation-maximizationGenome AtlasSuperior performanceHierarchical responseDisease outcomeClassificationPenalized estimatorsPractice settingsDisease modelsBiomedical studiesAnalysis literatureE effectsBayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data
Im Y, Li R, Ma S. Bayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data. Statistics In Medicine 2025, 44: e10350. PMID: 39840672, PMCID: PMC11774474, DOI: 10.1002/sim.10350.Peer-Reviewed Original Research
2024
CTHRC1+ fibroblasts and SPP1+ macrophages synergistically contribute to pro-tumorigenic tumor microenvironment in pancreatic ductal adenocarcinoma
Li E, Cheung H, Ma S. CTHRC1+ fibroblasts and SPP1+ macrophages synergistically contribute to pro-tumorigenic tumor microenvironment in pancreatic ductal adenocarcinoma. Scientific Reports 2024, 14: 17412. PMID: 39075108, PMCID: PMC11286765, DOI: 10.1038/s41598-024-68109-z.Peer-Reviewed Original ResearchConceptsPancreatic ductal adenocarcinomaTumor-associated macrophagesTumor microenvironmentEpithelial mesenchymal transitionDuctal adenocarcinomaImmune-suppressive tumor microenvironmentPro-tumorigenic tumor microenvironmentPancreatic cancer casesHeterogeneous tumor microenvironmentCombination of single-cellCancer-associated myofibroblastsSurgical resectionMyeloid cellsCurrent therapiesCancer casesLethal cancersSurvival rateExtracellular matrixTreat cancerMesenchymal transitionTherapeutic targetAdenocarcinomaCellular populationsCancerIntercellular interactionsHEARTSVG: 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 dataInformation‐incorporated sparse hierarchical cancer heterogeneity analysis
Han W, Zhang S, Ma S, Ren M. Information‐incorporated sparse hierarchical cancer heterogeneity analysis. Statistics In Medicine 2024, 43: 2280-2297. PMID: 38553996, PMCID: PMC12201913, DOI: 10.1002/sim.10071.Peer-Reviewed Original ResearchOrganochlorine pesticides and risk of papillary thyroid cancer in U.S. military personnel: a nested case-control study
Rusiecki J, McAdam J, Denic-Roberts H, Sjodin A, Davis M, Jones R, Hoang T, Ward M, Ma S, Zhang Y. Organochlorine pesticides and risk of papillary thyroid cancer in U.S. military personnel: a nested case-control study. Environmental Health 2024, 23: 28. PMID: 38504322, PMCID: PMC10949709, DOI: 10.1186/s12940-024-01068-0.Peer-Reviewed Original ResearchConceptsPapillary thyroid cancer riskNested Case-Control StudyPapillary thyroid cancerCase-control studyOdds ratioBody mass index categoriesOrganochlorine pesticidesConfidence intervalsFindings of increased riskRisk of papillary thyroid cancerConditional logistic regressionPre-diagnostic serum samplesIndividually-matched controlsThyroid cancerFollicular variant papillary thyroid cancerSerum concentrationsVariant papillary thyroid cancerEffect modificationU.S. military personnelBirth cohortPapillary thyroid cancer casesBirth yearIndex categoriesDecreased riskEffects of organochlorine pesticidesNeurogenetic underpinnings of nicotine use severity: Integrating the brain transcriptomes and GWAS variants via network approaches
Yang B, Xiang B, Wang T, Ma S, Li C. Neurogenetic underpinnings of nicotine use severity: Integrating the brain transcriptomes and GWAS variants via network approaches. Psychiatry Research 2024, 334: 115815. PMID: 38422867, PMCID: PMC11017751, DOI: 10.1016/j.psychres.2024.115815.Peer-Reviewed Original ResearchConceptsMediodorsal nucleus of the thalamusMedial prefrontal cortexPrefrontal cortexNeurogenetic underpinningsDorsolateral prefrontal cortexBrain transcriptomeDrug memoryOrbitofrontal cortexInhibitory controlSmoking severityAssociated with CPDHuman brain transcriptomeMediodorsal nucleusWhole-brainNeurogenetic mechanismsPrimary motor cortexMechanisms of individual variationCortexHub proteinsGenetic riskAmygdalaStriatumMotor cortexOPFCHippocampusEndocrine disrupting chemical mixture exposure and risk of papillary thyroid cancer in U.S. military personnel: A nested case-control study
Denic-Roberts H, McAdam J, Sjodin A, Davis M, Jones R, Ward M, Hoang T, Ma S, Zhang Y, Rusiecki J. Endocrine disrupting chemical mixture exposure and risk of papillary thyroid cancer in U.S. military personnel: A nested case-control study. The Science Of The Total Environment 2024, 922: 171342. PMID: 38428594, PMCID: PMC11034764, DOI: 10.1016/j.scitotenv.2024.171342.Peer-Reviewed Original ResearchConceptsPapillary thyroid cancer riskEndocrine Disrupting ChemicalsRisk of papillary thyroid cancerNested Case-Control StudyPolychlorinated biphenylsOne-quartile increaseCase-control studyPapillary thyroid cancerOdds ratioWhite raceEvaluate associationsThyroid cancer riskBrominated flame retardantsEDC mixturesHistological subtypesChemical mixture exposureNon-statistically significant increaseBody mass indexQuantile g-computationOrganochlorine pesticidesSerum sampling timesCancer riskThyroid cancerU.S. military personnelStandard error
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply