Search results for "cluster analysis."

showing 10 items of 805 documents

Cluster Analysis of Home Polygraphic Recordings in Symptomatic Habitually-Snoring Children: A Precision Medicine Perspective

2022

(1) Background: Sleep-disordered breathing (SDB) is a frequent problem in children. Cluster analyses offer the possibility of identifying homogeneous groups within a large clinical database. The application of cluster analysis to anthropometric and polysomnographic measures in snoring children would enable the detection of distinctive clinically-relevant phenotypes; (2) Methods: We retrospectively collected the results of nocturnal home-based cardiorespiratory polygraphic recordings and anthropometric measurements in 326 habitually-snoring otherwise healthy children. K-medoids clustering was applied to standardized respiratory and anthropometric measures, followed by Silhouette-based statis…

children; cluster analysis; obstructive sleep apnea; polygraphy; sleep apnea; sleep-disordered breathing; snoringpolygraphychildrensleep-disordered breathingGeneral Medicinesleep apneaobstructive sleep apneasnoringcluster analysisJournal of Clinical Medicine
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Phenazine antibiotics produced by fluorescent pseudomonads contribute to natural soil suppressiveness to Fusarium wilt

2009

Natural disease-suppressive soils provide an untapped resource for the discovery of novel beneficial microorganisms and traits. For most suppressive soils, however, the consortia of microorganisms and mechanisms involved in pathogen control are unknown. To date, soil suppressiveness to Fusarium wilt disease has been ascribed to carbon and iron competition between pathogenic Fusarium oxysporum and resident non-pathogenic F. oxysporum and fluorescent pseudomonads. In this study, the role of bacterial antibiosis in Fusarium wilt suppressiveness was assessed by comparing the densities, diversity and activity of fluorescent Pseudomonas species producing 2,4-diacetylphloroglucinol (DAPG) (phlD+) …

chlororaphis pcl1391Antifungal AgentsDISEASE SUPRESSIVE SOILMicroorganismColony Count Microbialdose-response relationshipsFLUORESCENT PSEUDOMONADSblack root-rotPlant Rootsgraminis var triticiFusariumSolanum lycopersicumFlaxCluster AnalysisFUSARIUM WILTPathogenPhylogenySoil Microbiologymedia_commonEcologyEPS-2genotypic diversityfood and beveragesBiodiversitygenetic diversityFusarium wilt[SDV.MP]Life Sciences [q-bio]/Microbiology and ParasitologyPHENAZINE ANTIBIOTICSPolymorphism Restriction Fragment LengthDNA BacterialGenotypemedia_common.quotation_subject2PhloroglucinolBiologyMicrobiologyCompetition (biology)MicrobiologyPseudomonasAntibiosisBotanyFusarium oxysporumEcology Evolution Behavior and Systematicsbiological-controlAntibiosisbiology.organism_classificationLaboratorium voor PhytopathologieLaboratory of Phytopathology24-diacetylphloroglucinol-producing pseudomonasoxysporum fo47PhenazinesBeneficial organismAntagonism4-diacetylphloroglucinol-producing pseudomonasnonpathogenic fusarium
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Serological and molecular characteristics of Vibrio vulnificus biotype 3: evidence for high clonality.

2007

Vibrio vulnificus biotype 3 has been implicated as the causative pathogen of an ongoing disease outbreak that erupted in Israel in 1996. Recent work based on multi-locus sequence typing (MLST) showed that V. vulnificus biotype 3 is genetically homogeneous. The aim of this study was to investigate the existence of subpopulations within this homogeneous biotype by characterizing the surface antigens and analysing the sequence diversity of selected outer-membrane protein (OMP)-encoding genes. Rabbit antisera were prepared against biotype 1, 2 and 3 strains. The results of the slide-agglutination test, dot-blot assay (using fresh and boiled cells), and immunoblotting of lipopolysaccharides (LPS…

clone (Java method)DNA BacterialLipopolysaccharidesPopulationImmunoblottingMolecular Sequence DataSequence HomologyBiologyMicrobiologyDNA sequencingMicrobiologyEvolution MolecularAgglutination TestsCluster AnalysisHumansTypingIsraeleducationGenePathogenVibrio vulnificuseducation.field_of_studyAntigens BacterialMolecular EpidemiologyBase SequenceStrain (biology)Genetic Variationbacterial infections and mycosesVibrio InfectionsbacteriaMultilocus sequence typingBacterial Outer Membrane ProteinsMicrobiology (Reading, England)
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Comparison of global visual field indices (MD,VFI), GPA II change and cluster analysis of visual field progression in glaucoma

2014

cluster analysis of visual fieldSettore MED/30 - Malattie Apparato Visivovisual field progression in glaucomavisual field indicevisual field indices; cluster analysis of visual field; visual field progression in glaucoma
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La valutazione di alcune cartteristiche del "prodotto vino" come indicatori di qualità mediante la Cluster Analysis

2008

cluster analysis qualità vino
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Multidimensional Clustering and Registration of Seismic Waveform Data

2011

cluster analysisSettore GEO/11 - Geofisica Applicata
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The Three Steps of Clustering in the Post-Genomic Era: A Synopsis

2011

Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. Following Handl et al., it can be summarized as a three step process: (a) choice of a distance function; (b) choice of a clustering algorithm; (c) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research. Unfortunately, the high dimensionality of the data and their noisy nature makes cluster analysis of genomic data particul…

cluster validation indicesSettore INF/01 - InformaticaProcess (engineering)Computer sciencebusiness.industryGenomic datadistance functionMachine learningcomputer.software_genreObject (computer science)ClusteringCluster algorithmPredictive powerRelevance (information retrieval)Artificial intelligenceHigh dimensionalitybusinessCluster analysiscomputer
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Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistic…

2008

Abstract Background Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, Data Analysis and Classification, in particular with regard to the prediction of the number of clusters in a dataset, usually established via internal validation measures. Despite the wealth of internal measures available in the literature, new ones have been recently proposed, some of them specifically for microarray data. Results We consider five such measures: Clest, Consensus (Consensus Clustering), FOM (Figure of Merit), Gap (Gap Statistics) and ME (Model Explorer), in addition to the classic WCSS (Within Cluster…

clustering microarray dataMicroarrayComputer scienceStatistics as Topiccomputer.software_genrelcsh:Computer applications to medicine. Medical informaticsBiochemistryStructural BiologyDatabases GeneticConsensus clusteringStatisticsCluster (physics)AnimalsCluster AnalysisHumansCluster analysislcsh:QH301-705.5Molecular BiologyOligonucleotide Array Sequence AnalysisStructure (mathematical logic)Microarray analysis techniquesApplied MathematicsComputational BiologyComputer Science ApplicationsBenchmarkingComputingMethodologies_PATTERNRECOGNITIONlcsh:Biology (General)Gene chip analysislcsh:R858-859.7Data miningDNA microarraycomputerAlgorithmsSoftwareResearch ArticleBMC Bioinformatics
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Data-Driven Interactive Multiobjective Optimization Using a Cluster-Based Surrogate in a Discrete Decision Space

2019

In this paper, a clustering based surrogate is proposed to be used in offline data-driven multiobjective optimization to reduce the size of the optimization problem in the decision space. The surrogate is combined with an interactive multiobjective optimization approach and it is applied to forest management planning with promising results. peerReviewed

data-driven optimizationMathematical optimizationOptimization problemComputer scienceboreal forest managementComputer Science::Neural and Evolutionary Computationpäätöksenteko0211 other engineering and technologiesMathematicsofComputing_NUMERICALANALYSISdecision maker02 engineering and technologypreference informationSpace (commercial competition)Multi-objective optimizationComputingMethodologies_ARTIFICIALINTELLIGENCEData-drivenklusteritoptimointi0202 electrical engineering electronic engineering information engineeringCluster analysis021103 operations researchsurrogatesComputingMethodologies_PATTERNRECOGNITIONboreaalinen vyöhyke020201 artificial intelligence & image processingmetsänhoitoCluster basedclustering
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Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R

2019

We present the R package clustrd which implements a class of methods that combine dimension reduction and clustering of continuous or categorical data. In particular, for continuous data, the package contains implementations of factorial K-means and reduced K-means; both methods combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means, i-FCB and cluster correspondence analysis, which combine multiple correspondence analysis with K-means. Two examples on real data sets are provided to illustrate the usage of the main functions.

dimension reduction; clustering; principal component analysis; multiple correspondence analysis; K-meansStatistics and Probabilitydimension reduction clustering principal component analysis multiple correspon-dence analysis K-meansFactorialmultiple correspon-dence analysisMultiple correspondence analysiComputer sciencedimension reductionprincipal component analysisk-meansmultiple correspondence analysisPrincipal component analysicomputer.software_genre01 natural sciencesCorrespondence analysis010104 statistics & probabilityMultiple correspondence analysis0101 mathematicsCluster analysisCategorical variablelcsh:Statisticslcsh:HA1-4737Dimensionality reductionk-means clusteringK-meanPrincipal component analysisData miningHA29-32Statistics Probability and UncertaintycomputerSoftwareclusteringJournal of Statistical Software
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