2025
Robust sparse Bayesian regression for longitudinal gene–environment interactions
Fan K, Jiang Y, Ma S, Wang W, Wu C. Robust sparse Bayesian regression for longitudinal gene–environment interactions. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2025, qlaf027. DOI: 10.1093/jrsssc/qlaf027.Peer-Reviewed Original ResearchCancer Prevention StudyGene-environment interactionsVariable selectionSpike-and-slab priorsGene-environmentIntra-cluster correlationBayesian variable selectionPrevention StudyMeasured body weightMeasures analysisLongitudinal studyPosterior inferenceGibbs samplerMCMC algorithmInteraction effectsStructured sparsityMixed modelsGenetic factorsExtensive simulationsFast computationPhenotypic measurementsInter-relatednessLongitudinal observationsANOVAInteraction problemsIntegrative 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 useConsistencyThe spike‐and‐slab quantile LASSO for robust variable selection in cancer genomics studies
Liu Y, Ren J, Ma S, Wu C. The spike‐and‐slab quantile LASSO for robust variable selection in cancer genomics studies. Statistics In Medicine 2024, 43: 4928-4983. PMID: 39260448, PMCID: PMC11585335, DOI: 10.1002/sim.10196.Peer-Reviewed Original ResearchAsymmetric Laplace distributionSpike-and-slab LASSORobust variable selection methodHeavy-tailed errorsRobust variable selectionHeavy-tailed distributionsAnalysis of high-dimensional genomic dataHigh-dimensional genomic dataExpectation-maximizationComprehensive simulation studyVariable selection methodsLaplace distributionCoordinate descent frameworkPosterior modeCancer genomics studiesRobust likelihoodVariable selectionSparsity patternSimulation studyComputational advantagesQuantile regressionNonrobust oneSelf-adaptationLoss functionGenomic studiesHigh-Dimensional Gene–Environment Interaction Analysis
Wu M, Li Y, Ma S. High-Dimensional Gene–Environment Interaction Analysis. Annual Review Of Statistics And Its Application 2024, 12: 361-383. PMID: 40881670, PMCID: PMC12383825, DOI: 10.1146/annurev-statistics-112723-034315.Peer-Reviewed Original ResearchFixed- and random-effects analysisG-E interaction analysisG-E interactionsVariable selectionFrequentist analysisGene-environmentRandom effects analysisGeneral frameworkStatistical propertiesProgression of complex diseasesDimension reductionHypothesis testingG-EComplex diseasesGenetic factorsInteraction analysisNonlinear effect analysisStatistical perspectiveDisease outcomeEnvironmental factorsPrediction-basedEstimation-basedA penalized integrative deep neural network for variable selection among multiple omics datasets
Li Y, Ren X, Yu H, Sun T, Ma S. A penalized integrative deep neural network for variable selection among multiple omics datasets. Quantitative Biology 2024, 12: 313-323. DOI: 10.1002/qub2.51.Peer-Reviewed Original ResearchOmics data analysisAvailability of omics dataMultiple omics datasetsGene expression datasetsAggregate multiple datasetsDeep neural networksOmics dataIntegrated deep neural networkOmics datasetsExpression datasetsMultiple datasetsDeep learningDiverse originsNeural networkOmicsAbstract Deep learningVariable selection resultsSample sizeVariable selectionIntegrated analysis frameworkCognitive statusOvarian cancer patientsModel interpretationExtensive simulation studyDataset
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