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-basedRobust 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 dataOutliersJoint modeling of mixed outcomes using a rank-based sparse neural network
Xue J, Xu Y, Li J, Ma S, Fang K. Joint modeling of mixed outcomes using a rank-based sparse neural network. Journal Of Biomedical Informatics 2025, 169: 104870. PMID: 40623577, PMCID: PMC12306493, DOI: 10.1016/j.jbi.2025.104870.Peer-Reviewed Original ResearchSparse neural networksNeural networkCompetitive performanceImbalance issueLoss functionSparse layerLeverage informationPrediction accuracyTraditional methodsNetworkParametric frameworkPenalization methodFaces challengesJoint modelPrediction modelInformationSkin cutaneous melanomaHigh-throughput profilingHigh-dimensional covariatesDimensionalityGenomic researchFeaturesMethodSimulation studyBiomedical studiesSubgroup Analysis of Differential Networks with Latent Variables
Li L, Ma S, Zhang Q. Subgroup Analysis of Differential Networks with Latent Variables. Statistics And Computing 2025, 35: 140. DOI: 10.1007/s11222-025-10681-z.Peer-Reviewed Original ResearchLow-rank structureSubgroup networksBaseline networkCompetitive performanceDifferential networksReal-world observational dataLatent variablesEfficient computational algorithmNetworkSparsityHeterogeneity analysis methodComputational algorithmInfluence of latent variablesDense networkSubgroup structureStatistical propertiesAlgorithmNetwork analysisSimulation studyMethodAnalysis methodDifferential network analysisIntegrative rank-based regression for multi-source high-dimensional data with multi-type responses
Xu F, Ma S, Zhang Q. Integrative rank-based regression for multi-source high-dimensional data with multi-type responses. Journal Of Applied Statistics 2025, 52: 2011-2030. PMID: 40904949, PMCID: PMC12404076, DOI: 10.1080/02664763.2025.2452964.Peer-Reviewed Original Research
2024
Integrative factor-adjusted sparse generalized linear models
Xu F, Ma S, Zhang Q. Integrative factor-adjusted sparse generalized linear models. Journal Of Statistical Computation And Simulation 2024, 95: 764-780. DOI: 10.1080/00949655.2024.2439450.Peer-Reviewed Original ResearchVariable selection consistencyHigh-dimensional dataIncreased accessibility of dataSelection consistencyConsistency propertiesCorrelated covariatesGeneralized linear modelVariable selectionAnalysis of genetic dataAccessibility of dataIdiosyncratic componentsCompetitive performanceCovariatesGenetic dataLinear modelSample sizeImprove model performanceEstimationIntegrated analysisModel estimatesLatent factorsModel performancePractical useConsistency
2021
Conditional score matching for high-dimensional partial graphical models
Fan X, Zhang Q, Ma S, Fang K. Conditional score matching for high-dimensional partial graphical models. Computational Statistics & Data Analysis 2021, 153: 107066. DOI: 10.1016/j.csda.2020.107066.Peer-Reviewed Original ResearchConditional scoreComputational complexityMore general distributionsEffective computational algorithmGraphical modelsStatistical propertiesHigh computational costPartial graphsComputational algorithmComputational costNormalization constantsBreast cancer gene expression datasetsGeneral distributionNetwork constructionCancer gene expression datasetsMultiplicative normalizationMultivariate data analysisGene expression datasetsCompetitive performanceComplexityGraphData analysisModelConstruction approachExpression datasets
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