Featured Publications
Integrated Single-Cell Atlas of Endothelial Cells of the Human Lung
Schupp JC, Adams TS, Cosme C, Raredon MSB, Yuan Y, Omote N, Poli S, Chioccioli M, Rose KA, Manning EP, Sauler M, DeIuliis G, Ahangari F, Neumark N, Habermann AC, Gutierrez AJ, Bui LT, Lafyatis R, Pierce RW, Meyer KB, Nawijn MC, Teichmann SA, Banovich NE, Kropski JA, Niklason LE, Pe’er D, Yan X, Homer RJ, Rosas IO, Kaminski N. Integrated Single-Cell Atlas of Endothelial Cells of the Human Lung. Circulation 2021, 144: 286-302. PMID: 34030460, PMCID: PMC8300155, DOI: 10.1161/circulationaha.120.052318.Peer-Reviewed Original ResearchConceptsDifferential expression analysisPrimary lung endothelial cellsLung endothelial cellsCell typesMarker genesExpression analysisSingle-cell RNA sequencing dataCross-species analysisVenous endothelial cellsEndothelial marker genesSingle-cell atlasMarker gene setsRNA sequencing dataEndothelial cellsSubsequent differential expression analysisDifferent lung cell typesResident cell typesLung cell typesCellular diversityEndothelial cell typesCapillary endothelial cellsHuman lung endothelial cellsPhenotypic diversityEndothelial diversityIndistinguishable populations
2019
LCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data
Sun J, Herazo-Maya JD, Wang JL, Kaminski N, Zhao H. LCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data. Statistical Applications In Genetics And Molecular Biology 2019, 18: 20170060. PMID: 30759070, DOI: 10.1515/sagmb-2017-0060.Peer-Reviewed Original Research
2018
Fibrosis: Lessons from OMICS analyses of the human lung
Yu G, Ibarra GH, Kaminski N. Fibrosis: Lessons from OMICS analyses of the human lung. Matrix Biology 2018, 68: 422-434. PMID: 29567123, PMCID: PMC6015529, DOI: 10.1016/j.matbio.2018.03.014.Peer-Reviewed Original ResearchConceptsIdiopathic pulmonary fibrosisDramatic phenotypic alterationsTranscriptomic studiesOmics analysisOmics profilingOmics technologiesPulmonary fibrosisNumerous aberrationsPhenotypic alterationsMechanistic understandingHuman idiopathic pulmonary fibrosisIPF lung tissueEpithelial cellsCentral roleHuman tissuesIPF samplesNew insightsMolecular featuresIPF lungsInflammatory cellsPatient cohortLung tissueAnimal modelsLethal disorderHuman lungiDREM: Interactive visualization of dynamic regulatory networks
Ding J, Hagood JS, Ambalavanan N, Kaminski N, Bar-Joseph Z. iDREM: Interactive visualization of dynamic regulatory networks. PLOS Computational Biology 2018, 14: e1006019. PMID: 29538379, PMCID: PMC5868853, DOI: 10.1371/journal.pcbi.1006019.Peer-Reviewed Original ResearchConceptsDynamic regulatory networksRegulatory networksHigh-throughput time series dataInteraction dataProtein-DNA interaction dataSingle-cell RNA-seqTime series gene expression dataStatic datasetsInteractive visualizationGene expression dataData typesRNA-seqTime series dataBiological processesExpression dataMiRNA expressionNetworkSeries dataImportant challengeNew versionDevelopmental dataNovel hypothesisUnified modelMultiple labsRecent years
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
2013
Expression of Regulatory Platelet MicroRNAs in Patients with Sickle Cell Disease
Jain S, Kapetanaki MG, Raghavachari N, Woodhouse K, Yu G, Barge S, Coronnello C, Benos PV, Kato GJ, Kaminski N, Gladwin MT. Expression of Regulatory Platelet MicroRNAs in Patients with Sickle Cell Disease. PLOS ONE 2013, 8: e60932. PMID: 23593351, PMCID: PMC3625199, DOI: 10.1371/journal.pone.0060932.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAnemia, Sickle CellBlood PlateletsCell LineChromosomes, Human, Pair 14Computational BiologyDown-RegulationFemaleGene Expression ProfilingGene Expression RegulationGenomic ImprintingHumansHydroxyureaMaleMegakaryocytesMicroRNAsMiddle AgedMolecular Sequence AnnotationOligonucleotide Array Sequence AnalysisReproducibility of ResultsTricuspid Valve InsufficiencyUp-RegulationYoung AdultConceptsMiRNA expression profilesExpression profilesMRNA targetsSignificant transcriptional repressionPlatelet miRNAsPost-transcriptional regulationMiRNA target sequencesComputational prediction analysisAltered miRNA expression profilesMRNA expression profilesExpression of miRNAsAgilent miRNA microarrayTranscriptional repressionPlatelet transcriptomeBiological pathwaysDownregulated miRNAsMiRNAsPlatelet transcriptsMiRNA microarrayPlatelet microRNAsTarget sequenceMiR-376aMiR-376QRT-PCRMiR-154
2012
An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection
Wang X, Kang DD, Shen K, Song C, Lu S, Chang LC, Liao SG, Huo Z, Tang S, Ding Y, Kaminski N, Sibille E, Lin Y, Li J, Tseng GC. An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics 2012, 28: 2534-2536. PMID: 22863766, PMCID: PMC3463115, DOI: 10.1093/bioinformatics/bts485.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsComputational BiologyGene Expression ProfilingGenomicsHumansMaleMeta-Analysis as TopicMicroarray AnalysisProstatic NeoplasmsQuality ControlSoftwareConceptsDifferent operation systemsMulti-core parallel computingUser-friendly softwareParallel computingPathway detectionSoftware suiteFlexible inputFast implementationOperation systemVisualization plotsSupplementary dataNew algorithmMetapathsNew challengesSummary outputMarker detectionPathway databasesLittle effortMeta-analysis pipelineRapid advancesHigh-throughput genomic technologiesGenomic dataSystematic pipelineComputingPipelineToward Systems Biology of Pulmonary Hypertension
Ahmad F, Champion HC, Kaminski N. Toward Systems Biology of Pulmonary Hypertension. Circulation 2012, 125: 1477-1479. PMID: 22371329, PMCID: PMC5115209, DOI: 10.1161/circulationaha.112.096396.Peer-Reviewed Original Research
2010
Module-based prediction approach for robust inter-study predictions in microarray data
Mi Z, Shen K, Song N, Cheng C, Song C, Kaminski N, Tseng GC. Module-based prediction approach for robust inter-study predictions in microarray data. Bioinformatics 2010, 26: 2586-2593. PMID: 20719761, PMCID: PMC2951088, DOI: 10.1093/bioinformatics/btq472.Peer-Reviewed Original Research
2009
Features of Mammalian microRNA Promoters Emerge from Polymerase II Chromatin Immunoprecipitation Data
Corcoran DL, Pandit KV, Gordon B, Bhattacharjee A, Kaminski N, Benos PV. Features of Mammalian microRNA Promoters Emerge from Polymerase II Chromatin Immunoprecipitation Data. PLOS ONE 2009, 4: e5279. PMID: 19390574, PMCID: PMC2668758, DOI: 10.1371/journal.pone.0005279.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsChromatin ImmunoprecipitationComputational BiologyCpG IslandsHumansMicroRNAsPromoter Regions, GeneticRNA Polymerase IIRNA, UntranslatedConceptsProtein coding genesMiRNA genesCoding genesIntragenic miRNAsPol II chromatin immunoprecipitationNon-coding RNA regulatorsRNA polymerase II promoterChromatin immunoprecipitation dataDiverse biological processesOwn unique promoterPolymerase II promoterTranscription start siteIntergenic miRNAsTranscription regulationMiRNA promotersRNA regulatorsChromatin immunoprecipitationPromoter organizationHost genesPrimary transcriptTranscript organizationStart siteImmunoprecipitation dataUnique promoterBiological processes
2007
Genomics and proteomics of lung disease: conference summary
Raj JU, Aliferis C, Caprioli RM, Cowley AW, Davies PF, Duncan MW, Erle DJ, Erzurum SC, Finn PW, Ischiropoulos H, Kaminski N, Kleeberger SR, Leikauf GD, Loyd JE, Martin TR, Matalon S, Moore JH, Quackenbush J, Sabo-Attwood T, Shapiro SD, Schnitzer JE, Schwartz DA, Schwiebert LM, Sheppard D, Ware LB, Weiss ST, Whitsett JA, Wurfel MM, Matthay MA. Genomics and proteomics of lung disease: conference summary. American Journal Of Physiology - Lung Cellular And Molecular Physiology 2007, 293: l45-l51. PMID: 17468134, PMCID: PMC4212816, DOI: 10.1152/ajplung.00139.2007.Peer-Reviewed Original Research
2005
From signatures to models: understanding cancer using microarrays
Segal E, Friedman N, Kaminski N, Regev A, Koller D. From signatures to models: understanding cancer using microarrays. Nature Genetics 2005, 37: s38-s45. PMID: 15920529, DOI: 10.1038/ng1561.Peer-Reviewed Original ResearchConceptsTranscriptional networksModel organismsRegulatory mechanismsBiological processesMolecular underpinningsMechanistic understandingModular organizationDisease mechanismsComputational analysisComprehensive viewGenomicsRobust signatureOrganismsMicroarrayComparative analysisMechanismSignaturesCellsCancerManagement of cancerAnalysis of Microarray Experiments for Pulmonary Fibrosis
Davé NB, Kaminski N. Analysis of Microarray Experiments for Pulmonary Fibrosis. Methods In Molecular Medicine 2005, 117: 333-358. PMID: 16118461, DOI: 10.1385/1-59259-940-0:333.Peer-Reviewed Original Research
2002
Statistical Methods for Analyzing Gene Expression Data for Cancer Research
Friedman N, Kaminski N. Statistical Methods for Analyzing Gene Expression Data for Cancer Research. Ernst Schering Foundation Symposium Proceedings 2002, 109-131. PMID: 12060998, DOI: 10.1007/978-3-662-04747-7_6.Peer-Reviewed Original Research
2000
Bioinformatics
Kaminski N. Bioinformatics. American Journal Of Respiratory Cell And Molecular Biology 2000, 23: 705-711. PMID: 11104721, DOI: 10.1165/ajrcmb.23.6.4291.Peer-Reviewed Original ResearchConceptsInformation overloadApplication of computersOverview of bioinformaticsData managementGlobal gene expression patternsUser's pointInternet connectionGenomic sequence informationGene expression patternsAvailable bioinformatics toolsAnalysis toolsBioinformaticsBioinformatics toolsModern biologySequence informationExpression patternsComputerMolecular biology labNew technologiesComputational methodsWide availabilityBiology labsRapid accumulationGeneral approachDatabase