2024
Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia
Ellis C, Miller R, Calhoun V. Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia. Frontiers In Psychiatry 2024, 15: 1165424. PMID: 38495909, PMCID: PMC10941842, DOI: 10.3389/fpsyt.2024.1165424.Peer-Reviewed Original ResearchHard clusteringNetwork dynamicsDynamic functional network connectivityFuzzy clustering frameworkExtract several featuresFuzzy clusteringC-meansExplainability approachesExplainability metricsData spaceClustering frameworkK-meansDynamic functional network connectivity stateNetwork connectivityModerate anticorrelationImage dataNetworkState dynamicsAnalysis frameworkConnectivity dynamicsFunctional network connectivityAnticorrelationCentroidFunctional magnetic resonance imaging dataFramework
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
2020
DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-Rate Variability (HRV) Data
Oskooei A, Chau S, Weiss J, Sridhar A, Martínez M, Michel B. DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-Rate Variability (HRV) Data. Studies In Computational Intelligence 2020, 914: 93-105. DOI: 10.1007/978-3-030-53352-6_9.Peer-Reviewed Original ResearchConvolutional autoencoderK-Nearest Neighbor ClassificationTraditional K-means clusteringFrequency domain featuresK-Nearest NeighborK-means clusteringShort-term memoryLSTM autoencoderUnsupervised methodDeep learningDomain featuresAutoencoderDBSCAN clusteringK-meansInterval time series dataUnsupervised identificationStress detectionData pointsTime series dataEngineering featuresNormal clusterLSTMSize of clustersComparing Image Segmentation Techniques for Determining 3D Orbital Cavernous Hemangioma Size on MRI.
Boparai R, Maeng M, Dunbar K, Godfrey K, Tooley A, Maher M, Kazim M. Comparing Image Segmentation Techniques for Determining 3D Orbital Cavernous Hemangioma Size on MRI. Ophthalmic Plastic And Reconstructive Surgery 2020, 36: 569-574. PMID: 32427734, DOI: 10.1097/iop.0000000000001651.Peer-Reviewed Original ResearchConceptsAverage tumor sizeTumor sizeInterobserver agreementHemangioma sizeOrbital cavernous hemangiomaConcordance correlation coefficientT2-weighted MRIEvaluate interobserver agreementCavernous hemangiomaLin's concordance correlation coefficientK-meansTumor boundariesSegmentation methodMRISignal intensitySubjective judgmentCorrelation coefficientImage segmentation methodImage segmentation techniquesParameter-dependent methodDegree of subjective judgmentK-means clusteringK-means clustering methodK-means clustering segmentationHemangioma
2006
Statistical 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
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