2025
Robust 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 dataOutliersPrecision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures
Zaboski B, Bednarek L. Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures. Journal Of Clinical Medicine 2025, 14: 2442. PMID: 40217892, PMCID: PMC11989962, DOI: 10.3390/jcm14072442.Peer-Reviewed Original ResearchNeural networkDeep learningObsessive-compulsive disorderApplication of deep learning architecturesRecurrent neural networkConvolutional neural networkDeep learning architectureHigh-dimensional dataAdversarial networkLearning architectureMultimodal datasetData generationObsessive-compulsive disorder researchNeural predictorsPredictors of treatment responseComplex psychiatric conditionsNetworkTreatment responseArchitecturePsychiatric conditionsPrecision psychiatryImplementation of DLDatasetClassificationLearningProbabilistic exponential family inverse regression and its applications
Pang D, Zhu R, Zhao H, Wang T. Probabilistic exponential family inverse regression and its applications. Biometrics 2025, 81: ujaf065. PMID: 40407023, DOI: 10.1093/biomtc/ujaf065.Peer-Reviewed Original ResearchConceptsExponential familyDouble exponential familyHigh-dimensional regressionLow-dimensional reductionHierarchical likelihoodData exampleInverse regressionDiscrete predictorsSimulation studyDiscrete dataHigh-dimensional dataParallelizable algorithmContinuous predictorsPresence–absence recordsDimension reductionResponse variablesAccumulation of high dimensional dataHigh-throughput sequencing technologyFactor model frameworkLatent factorsRecords of speciesSequence readsSingle-cell studiesSequencing technologiesCommunity ecologyIntegrative 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 useConsistencyLocal-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia
Xing Y, Pearlson G, Kochunov P, Calhoun V, Du Y. Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia. NeuroImage 2024, 299: 120839. PMID: 39251116, PMCID: PMC11491165, DOI: 10.1016/j.neuroimage.2024.120839.Peer-Reviewed Original ResearchConceptsSelection methodClassification accuracy gainsGraph-based regularizationHigh-dimensional dataFeature selection methodLocal structural informationSparse regularizationAblation studiesFeature subsetPublic datasetsFeature selectionClassification accuracyExperimental evaluationAccuracy gainsSelection techniquesNetwork connectivityData transformationSuperior performanceDatasetConvergence analysisStructural informationClassificationRegularizationFeaturesDisorder predictionAssessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy
Liao D, Liu C, Christensen B, Tong A, Huguet G, Wolf G, Nickel M, Adelstein I, Krishnaswamy S. Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy. 2024, 00: 1-6. DOI: 10.1109/ciss59072.2024.10480166.Peer-Reviewed Original ResearchMutual information neural estimatorMutual informationHigh-dimensional simulation dataHigh-dimensional dataNeural network representationSpectral entropyCIFAR-10Information-theoretic measuresClass labelsSTL-10Classification networkNeural representationSelf-supervisionSupervised learningIntrinsic dimensionalityClassification accuracyNeural networkAmbient dimensionNoise-resilientNeural estimatorNetwork initializationData geometryNetwork representationOverfittingNetwork
2023
Spectral top-down recovery of latent tree models
Aizenbud Y, Jaffe A, Wang M, Hu A, Amsel N, Nadler B, Chang J, Kluger Y. Spectral top-down recovery of latent tree models. Information And Inference A Journal Of The IMA 2023, 12: 2300-2350. PMID: 37593361, PMCID: PMC10431953, DOI: 10.1093/imaiai/iaad032.Peer-Reviewed Original ResearchLatent tree modelsHigh-dimensional dataLaplacian matrixFiedler vectorRandom wayTree structureObserved nodesGraphical modelsTerminal nodesTree modelTerms of runtimeNumber of samplesModerate sizeConquer approachRandom subsetCertain conditionsSimilar accuracyPrevious methodsSmall subtreesCommon approachModelHigh probabilityOnly observationScientific domainsNon-random way
2021
Promote sign consistency in the joint estimation of precision matrices
Zhang Q, Ma S, Huang Y. Promote sign consistency in the joint estimation of precision matrices. Computational Statistics & Data Analysis 2021, 159: 107210. DOI: 10.1016/j.csda.2021.107210.Peer-Reviewed Original ResearchMultiple precision matricesPrecision matrixRegularization methodJoint estimationGroup parametersSign consistencyConsistency propertiesGaussian graphical modelsNovel regularization methodHigh-dimensional dataRandom variablesSparsity structureData examplesMore interpretable resultsNatural interpretationConditional independenceInterpretable resultsGraphical modelsPractical examplesEstimationConflicting signsPopular toolMatrixParametersFull flexibilityThe geometry of clinical labs and wellness states from deeply phenotyped humans
Zimmer A, Korem Y, Rappaport N, Wilmanski T, Baloni P, Jade K, Robinson M, Magis A, Lovejoy J, Gibbons S, Hood L, Price N. The geometry of clinical labs and wellness states from deeply phenotyped humans. Nature Communications 2021, 12: 3578. PMID: 34117230, PMCID: PMC8196202, DOI: 10.1038/s41467-021-23849-8.Peer-Reviewed Original Research
2018
A Forward and Backward Stagewise algorithm for nonconvex loss functions with adaptive Lasso
Shi X, Huang Y, Huang J, Ma S. A Forward and Backward Stagewise algorithm for nonconvex loss functions with adaptive Lasso. Computational Statistics & Data Analysis 2018, 124: 235-251. PMID: 30319163, PMCID: PMC6181148, DOI: 10.1016/j.csda.2018.03.006.Peer-Reviewed Original ResearchNonconvex loss functionsHigh-dimensional settingsLoss functionConvex loss functionsAdaptive LASSO penaltySecond-order derivativesStagewise algorithmHigh-dimensional dataExtensive numerical studyApproximate solutionAdaptive lassoRank estimationLasso penaltyComputational algorithmStationary pointsEffective algorithmImportant applicationsNumerical studyAlgorithmPopular toolRobust resultsEstimationPenalizationProblemLASSO
2017
exprso: an R-package for the rapid implementation of machine learning algorithms
Quinn T, Tylee D, Glatt S. exprso: an R-package for the rapid implementation of machine learning algorithms. F1000Research 2017, 5: 2588. PMID: 29560250, PMCID: PMC5832912, DOI: 10.12688/f1000research.9893.2.Peer-Reviewed Original ResearchNon-expert programmersObject-oriented frameworkMulti-class classificationHigh-dimensional dataMachine learningEnsemble classificationIntuitive machineCross-validation schemeR packageFeature selectionNew R packageProgrammersInterchangeable modulesGeneralizable modelMachineClassificationRapid implementationModuleAlgorithmLearningImplementationPipelineSchemeFrameworkPredictionA Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix
Hu Z, Dong K, Dai W, Tong T. A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix. The International Journal Of Biostatistics 2017, 13: 20170013. PMID: 28953454, DOI: 10.1515/ijb-2017-0013.Peer-Reviewed Original ResearchConceptsHigh-dimensional covariance matricesCovariance matrixCovariance matrix estimationMatrix estimation methodExtensive simulation studyHigh-dimensional dataStatistical inferenceCovariance matrix estimation methodMatrix estimationComputational challengesInformation theoryEstimation methodSimulation studyHigh dimensionalityLoss functionStatistical testsComparison resultsReal applicationsInteresting comparison resultsComparison of methodsMatrixRecent proposalSample sizeDimensionalityTheoryIntroduction to Bayesian variable selection methods in high-dimensional omics data analysis
Dong X, Xu S, Tao R, Wang T. Introduction to Bayesian variable selection methods in high-dimensional omics data analysis. Chinese Journal Of Epidemiology 2017, 38: 679-683. PMID: 28651411, DOI: 10.3760/cma.j.issn.0254-6450.2017.05.025.Peer-Reviewed Original ResearchConceptsBayesian variable selection methodOmics dataBayesian variable selectionAnalysis of high-dimensional dataDevelopment of genome sequencing technologyVariable selection methodsHigh-dimensional omics data analysisCase nGenome sequencing technologiesOmics data analysisVariable selectionVariable PHigh-dimensional dataSequencing technologiesStatistical challengesMeasure thousandsOmicsProgression of diseaseBioinformaticsBayesian Variable Selection Methods for Matched Case-Control Studies
Asafu-Adjei J, Tadesse M, Coull B, Balasubramanian R, Lev M, Schwamm L, Betensky R. Bayesian Variable Selection Methods for Matched Case-Control Studies. The International Journal Of Biostatistics 2017, 13: 20160043. PMID: 28157692, PMCID: PMC5505078, DOI: 10.1515/ijb-2016-0043.Peer-Reviewed Original Research
2016
exprso: an R-package for the rapid implementation of machine learning algorithms
Quinn T, Tylee D, Glatt S. exprso: an R-package for the rapid implementation of machine learning algorithms. F1000Research 2016, 5: 2588. DOI: 10.12688/f1000research.9893.1.Peer-Reviewed Original ResearchNon-expert programmersObject-oriented frameworkMulti-class classificationHigh-dimensional dataNative supportMachine learningEnsemble classificationIntuitive machineCross-validation schemeR packageFeature selectionBinary classifierNew R packageSimplicity of useProgrammersInterchangeable modulesMachineGeneralizable modelClassificationRapid implementationModuleClassifierSuiteAlgorithmLearning
2015
Classification of Schizophrenia and Bipolar Patients Using Static and Time-Varying Resting-State FMRI Brain Connectivity
Rashid B, Arbabshirani M, Damaraju E, Millar R, Cetin M, Pearlson G, Calhoun V. Classification of Schizophrenia and Bipolar Patients Using Static and Time-Varying Resting-State FMRI Brain Connectivity. 2015, 251-254. DOI: 10.1109/isbi.2015.7163861.Peer-Reviewed Original ResearchClassification of schizophreniaHigh-dimensional dataAutomatic differential diagnosisAutomatic classificationAccurate classifierDimensional dataChallenging taskNetwork connectivityDiscriminative analysisHigh accuracyPowerful informationClassificationTraining subjectsLarge amountPrevious workDynamic functional network connectivityConnectivityClassifierFunctional network connectivityFNC analysisTaskBrain connectivityRobustnessFrameworkAccuracyInferring biological tasks using Pareto analysis of high-dimensional data
Hart Y, Sheftel H, Hausser J, Szekely P, Ben-Moshe N, Korem Y, Tendler A, Mayo A, Alon U. Inferring biological tasks using Pareto analysis of high-dimensional data. Nature Methods 2015, 12: 233-235. PMID: 25622107, DOI: 10.1038/nmeth.3254.Peer-Reviewed Original Research
2014
Diffusion maps for changing data
Coifman R, Hirn M. Diffusion maps for changing data. Applied And Computational Harmonic Analysis 2014, 36: 79-107. DOI: 10.1016/j.acha.2013.03.001.Peer-Reviewed Original ResearchParameter spaceDiffusion mapsHigh-dimensional dataLow-dimensional spaceApproximation theoremGraph LaplacianIntrinsic geometryDimensional spaceSet of parametersNonlinear mappingDimensional dataGlobal behaviorEmbedding changesSpaceTypes of dataTheoremPowerful toolLaplacianGraphGeometryTermsEmbeddingDistanceParameters
2012
SHARE: system design and case studies for statistical health information release
Gardner J, Xiong L, Xiao Y, Gao J, Post A, Jiang X, Ohno-Machado L. SHARE: system design and case studies for statistical health information release. Journal Of The American Medical Informatics Association 2012, 20: 109-116. PMID: 23059729, PMCID: PMC3555328, DOI: 10.1136/amiajnl-2012-001032.Peer-Reviewed Original ResearchConceptsDifferential privacy frameworkPrivacy frameworkDifferential privacyMultidimensional histogramsReal medical datasetsMedical data warehouseOriginal data distributionInformation releaseHigh-dimensional dataBreast cancer datasetPattern queriesMedical datasetsElectronic medical record datasetHeterogeneous dataData warehouseUse casesElectronic health recordsMedical domainBiomedical dataThree-dimensional data cubeArt methodsData distributionMedical dataDimensional dataData cube
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