2018
Regularized Latent Class Model for Joint Analysis of High-Dimensional Longitudinal Biomarkers and a Time-to-Event Outcome
Sun J, Herazo-Maya J, Molyneaux PL, Maher TM, Kaminski N, Zhao H. Regularized Latent Class Model for Joint Analysis of High-Dimensional Longitudinal Biomarkers and a Time-to-Event Outcome. Biometrics 2018, 75: 69-77. PMID: 30178494, DOI: 10.1111/biom.12964.Peer-Reviewed Original ResearchConceptsJoint latent class modelLongitudinal biomarkersExtensive simulation studyLatent class modelLongitudinal submodelJoint modeling methodSurvival submodelLikelihood approachSimulation studyClass modelEvent outcomesLatent classesModeling methodMembership modelRandom effectsModeling approachClassSubmodelsJoint analysisModelBootstrapUnique trajectoriesNovel biological insightsInference
2017
A Dirichlet process mixture model for clustering longitudinal gene expression data
Sun J, Herazo‐Maya J, Kaminski N, Zhao H, Warren JL. A Dirichlet process mixture model for clustering longitudinal gene expression data. Statistics In Medicine 2017, 36: 3495-3506. PMID: 28620908, PMCID: PMC5583037, DOI: 10.1002/sim.7374.Peer-Reviewed Original ResearchConceptsLongitudinal gene expression profilesDirichlet process prior distributionRegression coefficientsExtensive simulation studyLongitudinal gene expression dataBayesian settingPrior distributionClustering methodFactor analysis modelDimensionality challengeStatistical methodsSimulation studyNovel clustering methodHigh dimensionality challengeSubgroup identificationImportant problemGene expression dataInteresting subgroupsClusteringCoefficientAnalysis modelModelExpression data
2015
Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes
Kim S, Herazo-Maya JD, Kang DD, Juan-Guardela BM, Tedrow J, Martinez FJ, Sciurba FC, Tseng GC, Kaminski N. Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes. BMC Genomics 2015, 16: 924. PMID: 26560100, PMCID: PMC4642618, DOI: 10.1186/s12864-015-2170-4.Peer-Reviewed Original Research
2014
T-RECS: STABLE SELECTION OF DYNAMICALLY FORMED GROUPS OF FEATURES WITH APPLICATION TO PREDICTION OF CLINICAL OUTCOMES
Altman R, Dunker A, Hunter L, Ritchie M, Murray T, Klein T, HUANG G, TSAMARDINOS I, RAGHU V, KAMINSKI N, BENOS P. T-RECS: STABLE SELECTION OF DYNAMICALLY FORMED GROUPS OF FEATURES WITH APPLICATION TO PREDICTION OF CLINICAL OUTCOMES. Biocomputing 2014, 20: 431-42. PMID: 25592602, PMCID: PMC4299881, DOI: 10.1142/9789814644730_0041.Peer-Reviewed Original ResearchConceptsTraditional feature selection methodsFeature selection methodCohort of patientsPersonalized medicine strategiesReal expression dataFeature selectionClassification accuracyCluster selectionBiological datasetsClinical outcomesCluster featuresLung diseaseBreast cancerSelection methodPatient classificationStructured natureMedicine strategiesSurvival dataTarget variablesEfficient selectionCohortStable selectionImportant featuresMissing value imputation in high-dimensional phenomic data: imputable or not, and how?
Liao SG, Lin Y, Kang DD, Chandra D, Bon J, Kaminski N, Sciurba FC, Tseng GC. Missing value imputation in high-dimensional phenomic data: imputable or not, and how? BMC Bioinformatics 2014, 15: 346. PMID: 25371041, PMCID: PMC4228077, DOI: 10.1186/s12859-014-0346-6.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsCluster AnalysisComputational BiologyComputer SimulationDatasets as TopicEpidemiologic MethodsHumansResearch DesignSoftwareConceptsImputation methodsSTS schemeReal data analysisData imputationMissing valuesDifferent imputation methodsBest imputation methodOrdinal data typeComplete data matrixValue imputation methodsMultivariate imputationWeighted hybridData matrixR packageValue imputationContinuous intensityImputation errorPhenomic dataSelection schemeReal datasetsSchemeMost methodsImputationSimulationsMicroarray experiments
2005
Comparison of normalization methods for CodeLink Bioarray data
Wu W, Dave N, Tseng GC, Richards T, Xing EP, Kaminski N. Comparison of normalization methods for CodeLink Bioarray data. BMC Bioinformatics 2005, 6: 309. PMID: 16381608, PMCID: PMC1373657, DOI: 10.1186/1471-2105-6-309.Peer-Reviewed Original Research