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 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 problems
2024
High-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-based
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