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
A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis
Huang Y, Ma SF, Vij R, Oldham JM, Herazo-Maya J, Broderick SM, Strek ME, White SR, Hogarth DK, Sandbo NK, Lussier YA, Gibson KF, Kaminski N, Garcia JG, Noth I. A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis. BMC Pulmonary Medicine 2015, 15: 147. PMID: 26589497, PMCID: PMC4654815, DOI: 10.1186/s12890-015-0142-8.Peer-Reviewed Original ResearchConceptsIdiopathic pulmonary fibrosisPrognostic indexIPF patientsPulmonary fibrosisValidation cohortTraining cohortMultivariate Cox regression survival analysisPrognostic modelPeripheral blood mononuclear cellsUnivariate Cox regression analysisCox regression survival analysisLow-risk patientsWeighted gene co-expression network analysisCox regression analysisBlood mononuclear cellsCourse of diseaseIndependent validation cohortRegression survival analysisNovel prognostic modelPredictor genesT cell biologyT cell receptorCurrent prognostic toolsFunctional pathway analysisFold change
2013
Reconstructing dynamic microRNA-regulated interaction networks
Schulz MH, Pandit KV, Cardenas C, Ambalavanan N, Kaminski N, Bar-Joseph Z. Reconstructing dynamic microRNA-regulated interaction networks. Proceedings Of The National Academy Of Sciences Of The United States Of America 2013, 110: 15686-15691. PMID: 23986498, PMCID: PMC3785769, DOI: 10.1073/pnas.1303236110.Peer-Reviewed Original ResearchConceptsTranscription factorsGene expressionDynamic Regulatory Events MinerTemporal gene expressionDynamic regulatory networksSpecific developmental phasesMRNA expression dataLung developmentRegulatory networksMiRNA targetsInteraction networksImportant miRNAsExpression dataMiRNAsAdditional miRNAsLung differentiationDevelopmental phasesMiRNAPostnatal lung developmentProgression pathwaysProliferation assaysExpressionRegulationMRNA expressionMicroRNAs
2007
A Patient-Gene Model for Temporal Expression Profiles in Clinical Studies
Kaminski N, Bar-Joseph Z. A Patient-Gene Model for Temporal Expression Profiles in Clinical Studies. Journal Of Computational Biology 2007, 14: 324-338. PMID: 17563314, DOI: 10.1089/cmb.2007.0001.Peer-Reviewed Original ResearchConceptsClinical studiesResponse ratePatient expression dataDisease progressionPatient levelPatient responseExpression profilesResponse patternsBaseline expressionPatient dataCommon response patternExpression levelsPatientsCell linesSpecific response patternsTemporal expression levelsLab animalsExpression patternsGene levelSpecific expression patternsImportant pathwayLevelsTemporal expression profiles
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