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
Missing 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