2021
A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data
Li H, Zhu B, Xu Z, Adams T, Kaminski N, Zhao H. A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data. BMC Bioinformatics 2021, 22: 524. PMID: 34702190, PMCID: PMC8549347, DOI: 10.1186/s12859-021-04412-0.Peer-Reviewed Original ResearchConceptsMarkov random field modelRandom field modelMean field-like approximationField modelSpecific DEGsExpectation maximizationSingle-cell sequencing technologiesProtein-coding genesRNA sequencing data setsSingle-cell RNA-seq dataCell-type levelCell typesGibbs samplerSingle-cell RNA sequencing data setsCell-cell networksDifferential expression analysisRNA-seq dataGene network informationStatistical powerType I error ratesDifferent expression levelsMRF modelI error rateModel parametersBiological networks
2020
Tocilizumab Treatment for Cytokine Release Syndrome in Hospitalized Patients With Coronavirus Disease 2019 Survival and Clinical Outcomes
Price CC, Altice FL, Shyr Y, Koff A, Pischel L, Goshua G, Azar MM, Mcmanus D, Chen SC, Gleeson SE, Britto CJ, Azmy V, Kaman K, Gaston DC, Davis M, Burrello T, Harris Z, Villanueva MS, Aoun-Barakat L, Kang I, Seropian S, Chupp G, Bucala R, Kaminski N, Lee AI, LoRusso PM, Topal JE, Dela Cruz C, Malinis M. Tocilizumab Treatment for Cytokine Release Syndrome in Hospitalized Patients With Coronavirus Disease 2019 Survival and Clinical Outcomes. CHEST Journal 2020, 158: 1397-1408. PMID: 32553536, PMCID: PMC7831876, DOI: 10.1016/j.chest.2020.06.006.Peer-Reviewed Original ResearchConceptsCytokine release syndromeTocilizumab-treated patientsSevere diseaseRelease syndromeTocilizumab treatmentInflammatory biomarkersNonsevere diseaseSoluble IL-2 receptor levelsHigh-sensitivity C-reactive proteinIL-2 receptor levelsConsecutive COVID-19 patientsIL-6 receptor antagonistMechanical ventilation outcomesC-reactive proteinCOVID-19 patientsHigher admission levelsRace/ethnicityMV daysVentilation outcomesAdverse eventsChart reviewClinical responseMedian ageWhite patientsClinical outcomes
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
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 ResearchConceptsImputation 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 experimentsBidirectional elastic image registration using B-spline affine transformation
Gu S, Meng X, Sciurba FC, Ma H, Leader J, Kaminski N, Gur D, Pu J. Bidirectional elastic image registration using B-spline affine transformation. Computerized Medical Imaging And Graphics 2014, 38: 306-314. PMID: 24530210, PMCID: PMC4019704, DOI: 10.1016/j.compmedimag.2014.01.002.Peer-Reviewed Original ResearchAlgorithmsHumansImaging, Three-DimensionalNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedRadiography, ThoracicReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueTomography, X-Ray Computed
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 expressionMicroRNAsAssessment of lung volume collapsibility in chronic obstructive lung disease patients using CT
Kundu S, Gu S, Leader JK, Tedrow JR, Sciurba FC, Gur D, Kaminski N, Pu J. Assessment of lung volume collapsibility in chronic obstructive lung disease patients using CT. European Radiology 2013, 23: 1564-1572. PMID: 23494492, PMCID: PMC3657332, DOI: 10.1007/s00330-012-2746-1.Peer-Reviewed Original ResearchConceptsPulmonary function testsUpper lobeLower lobeWhole lungLung lobesChronic obstructive lung disease patientsDisease severityGOLD categoriesObstructive lung disease patientsLung disease patientsMean lung densityTotal lung volumeAssessment of lungLobar volume changesFEV1/Lung functionExpiration volumeFunction testsGOLD classificationInspiration/expirationDisease patientsRV/Air trappingLung volumeCT examinations
2012
Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density
Coronnello C, Hartmaier R, Arora A, Huleihel L, Pandit KV, Bais AS, Butterworth M, Kaminski N, Stormo GD, Oesterreich S, Benos PV. Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density. PLOS Computational Biology 2012, 8: e1002830. PMID: 23284279, PMCID: PMC3527281, DOI: 10.1371/journal.pcbi.1002830.Peer-Reviewed Original ResearchAn 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 ResearchConceptsDifferent 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 pipelineComputingPipeline
2011
Finding subtypes of transcription factor motif pairs with distinct regulatory roles
Bais AS, Kaminski N, Benos PV. Finding subtypes of transcription factor motif pairs with distinct regulatory roles. Nucleic Acids Research 2011, 39: e76-e76. PMID: 21486752, PMCID: PMC3113591, DOI: 10.1093/nar/gkr205.Peer-Reviewed Original ResearchConceptsTF binding sitesTranscription factorsDownstream regulationMotif pairsTF-DNA binding specificityBinding preferencesDNA binding specificityDNA binding preferencesDistinct regulatory rolesDownstream regulatory effectsMultiple regulatory pathwaysDifferent binding preferencesDyad motifDNA sequencesSequence elementsRegulatory pathwaysBinding specificityRegulatory roleDifferential recruitmentBinding sitesMotif discoveryRegulationCofactorMotifDistinct modes
2008
Alignment and classification of time series gene expression in clinical studies
Lin TH, Kaminski N, Bar-Joseph Z. Alignment and classification of time series gene expression in clinical studies. Bioinformatics 2008, 24: i147-i155. PMID: 18586707, PMCID: PMC2718630, DOI: 10.1093/bioinformatics/btn152.Peer-Reviewed Original Research
2007
A Functional and Regulatory Map of Asthma
Novershtern N, Itzhaki Z, Manor O, Friedman N, Kaminski N. A Functional and Regulatory Map of Asthma. American Journal Of Respiratory Cell And Molecular Biology 2007, 38: 324-336. PMID: 17921359, PMCID: PMC2258452, DOI: 10.1165/rcmb.2007-0151oc.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAllergensAnimalsAsthmaDisease Models, AnimalGene Expression ProfilingHumansHypersensitivityImmunity, InnateInterleukin-13MiceMice, Inbred AMice, Inbred BALB CMice, Inbred C3HMice, KnockoutModels, BiologicalOligonucleotide Array Sequence AnalysisOvalbuminProtein Interaction MappingReproducibility of ResultsSystems BiologyTranscription, GeneticTransforming Growth Factor beta1ConceptsCo-regulated gene modulesGene expression compendiumProtein interaction networksModule network analysisMouse microarray datasetsSystems-level viewExpression compendiumRegulatory mapGene modulesModule membersFunctional themesInteraction networksKey regulatorAnimal modelsMicroarray datasetsGeneral inductionAnnotation setsChronic inflammatory airway diseasesMorbidity of asthmaInflammatory airway diseasesMechanisms of asthmaAdaptive immune responsesSystem-level approachSimilar roleDistinct responsesA 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 ResearchFrom 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
2004
Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays
Barash Y, Dehan E, Krupsky M, Franklin W, Geraci M, Friedman N, Kaminski N. Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays. Bioinformatics 2004, 20: 839-846. PMID: 14751998, DOI: 10.1093/bioinformatics/btg487.Peer-Reviewed Original Research