2023
Statistical Methodologies for Analyzing Genomic Data
Duan F, Zhang H. Statistical Methodologies for Analyzing Genomic Data. Springer Handbooks 2023, 621-634. DOI: 10.1007/978-1-4471-7503-2_32.Peer-Reviewed Original ResearchLinear discriminant analysisEmpirical Bayesian approachDifferent clustering methodsModel-based clusteringNeural networkStatistical methodologyK-meansVector machineMicroarray data analysisColon cancer datasetBayesian approachClassification methodRand indexStatistical issuesClustering methodMultiple comparison issuesMicroarray dataCancer datasetsComparison issuesHierarchical clusteringT-statisticAlgorithmClassificationClusteringGenomic data
2018
Subtype classification and heterogeneous prognosis model construction in precision medicine
You N, He S, Wang X, Zhu J, Zhang H. Subtype classification and heterogeneous prognosis model construction in precision medicine. Biometrics 2018, 74: 814-822. PMID: 29359319, DOI: 10.1111/biom.12843.Peer-Reviewed Original ResearchConceptsRegularization regressionVariable selectionHigh-dimensional predictorsNecessary statistical methodsVariable selection methodsExpectation-maximization algorithmOracle propertyPenalty parameterSemiparametric modelStatistical methodsParametric modelNumerical calculationsProper choiceModel constructionSelection methodGene expression datasetsModelEstimatorSubtype-specific risk factorsRegularizerSurvival probabilityHigh-throughput technologiesExpression datasetsAlgorithmSimulations
2017
modSaRa: a computationally efficient R package for CNV identification
Xiao F, Niu Y, Hao N, Xu Y, Jin Z, Zhang H. modSaRa: a computationally efficient R package for CNV identification. Bioinformatics 2017, 33: 2384-2385. PMID: 28453611, PMCID: PMC5860124, DOI: 10.1093/bioinformatics/btx212.Peer-Reviewed Original ResearchConceptsComputational complexityHigh computational complexityR packageOptimal computational complexityUser-friendly R packageEfficient R packageSupplementary dataCurrent versionComputational algorithmDesirable accuracyComplexityPackageComprehensive toolCNV identificationDownloadAlgorithmVariant identificationBioinformaticsImplementationTypes of variationRCPPAccuracyIntegrationStepTool
2012
Simulating Realistic Genomic Data With Rare Variants
Xu Y, Wu Y, Song C, Zhang H. Simulating Realistic Genomic Data With Rare Variants. Genetic Epidemiology 2012, 37: 163-172. PMID: 23161487, PMCID: PMC3543480, DOI: 10.1002/gepi.21696.Peer-Reviewed Original ResearchTHE SCREENING AND RANKING ALGORITHM TO DETECT DNA COPY NUMBER VARIATIONS.
Niu YS, Zhang H. THE SCREENING AND RANKING ALGORITHM TO DETECT DNA COPY NUMBER VARIATIONS. The Annals Of Applied Statistics 2012, 6: 1306-1326. PMID: 24069112, PMCID: PMC3779928, DOI: 10.1214/12-aoas539supp.Peer-Reviewed Original ResearchThe screening and ranking algorithm to detect DNA copy number variations
Niu Y, Zhang H. The screening and ranking algorithm to detect DNA copy number variations. The Annals Of Applied Statistics 2012, 6: 1306-1326. DOI: 10.1214/12-aoas539.Peer-Reviewed Original ResearchDNA copy number variationsSource of genetic variationGenome analysis platformHigh-throughput dataCopy number variationsRanking algorithmGenetic variationNumber variationsPhenotypic differencesTheoretical propertiesThroughput dataAnalysis platformAlgorithmCharacterization algorithmGenomeComputational methods
2006
A statistical framework for the classification of tensor morphologies in diffusion tensor images
Zhu H, Xu D, Raz A, Hao X, Zhang H, Kangarlu A, Bansal R, Peterson BS. A statistical framework for the classification of tensor morphologies in diffusion tensor images. Magnetic Resonance Imaging 2006, 24: 569-582. PMID: 16735178, PMCID: PMC2367261, DOI: 10.1016/j.mri.2006.01.004.Peer-Reviewed Original ResearchStatistical Methodologies for Analyzing Genomic Data
Duan F, Zhang H. Statistical Methodologies for Analyzing Genomic Data. Springer Handbooks 2006, 607-621. DOI: 10.1007/978-1-84628-288-1_33.Peer-Reviewed Original ResearchLinear discriminant analysisDifferent clustering methodsEmpirical Bayesian approachModel-based clusteringNeural networkK-meansVector machineMicroarray data analysisColon cancer datasetClassification methodRand indexStatistical methodologyClustering methodBayesian approachMicroarray dataStatistical issuesMultiple comparison issuesComparison issuesHierarchical clusteringT-statisticAlgorithmClassificationClusteringGenomic dataClassification analysis
1997
Multivariate Adaptive Splines for Analysis of Longitudinal Data
Zhang H. Multivariate Adaptive Splines for Analysis of Longitudinal Data. Journal Of Computational And Graphical Statistics 1997, 6: 74-91. DOI: 10.1080/10618600.1997.10474728.Peer-Reviewed Original ResearchSemi-parametric modelNonparametric modelLongitudinal dataMultivariate adaptive splinesGeneral time trendExistence of autocorrelationMultivariate adaptive regression splinesRandom-effects linear modelsAdaptive regression splinesAdaptive splinesCovariance estimationDesign matrixRegression splinesFast algorithmTime trendsIterative procedureGeneral algorithmLinear modelSplinesModel buildingMean curveEstimationAlgorithmData structureEssential features