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 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 experiments
2002
Practical Approaches to Analyzing Results of Microarray Experiments
Kaminski N, Friedman N. Practical Approaches to Analyzing Results of Microarray Experiments. American Journal Of Respiratory Cell And Molecular Biology 2002, 27: 125-132. PMID: 12151303, DOI: 10.1165/ajrcmb.27.2.f247.Peer-Reviewed Original ResearchConceptsCommon clustering methodsStatistical meaningLarge-scale gene expression dataMicroarray experimentsClustering methodGene expression dataProblemAdvanced toolsValid directionsMain challengesExpression dataApproachEfficient useTechnologySolutionPractical focusPractical approachSuccessful implementation