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Objectives: To identify novel asthma phenotypes using an unsupervised hierarchicalclusteranalysis.
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Using hierarchicalclusteranalysis, we divided EBV-positive gastric carcinomas into two clusters.
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These genes were further classified into 26 profiles by hierarchicalclusteranalysis.
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Explanatory typologies were developed and then validated through hierarchicalclusteranalysis.
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Conclusions: Five distinct clinical phenotypes of asthma have been identified using unsupervised hierarchicalclusteranalysis.
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The results were analyzed using hierarchicalclusteranalysis and chi(2) test for trend.
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Statistical analysis was performed making use of a factor analysis and a hierarchicalclusteranalysis.
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Multidimensional scaling and hierarchicalclusteranalysis were used to produce the conceptual model (maps).
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Unsupervised hierarchicalclusteranalysis showed distinct profiles between the bulky ESCC specimens and normal epithelial specimens.
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We then examined the patterns of genetic correlation through hierarchicalclusteranalysis and by principal components analysis.
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Also, unsupervised hierarchicalclusteranalysis revealed distinct profiles between the biopsy ESCC samples and normal epithelial specimens.
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Both hierarchicalclusteranalysis and principal component analysis have been used to distinguish HP seed oil from different varieties.
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Results: Unsupervised hierarchicalclusteranalysis revealed that the samples from LTx patients can be clearly distinguished from the comparison group.
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The principal components analysis (PCA) and hierarchicalclusteranalysis showed a mixing of avocado trees from different districts.
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Using simulated data, we found that factor analysis clearly identifies the number and structure of factors and outperforms hierarchicalclusteranalysis.
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Symptom scores were compared between treatment groups, and hierarchicalclusteranalysis was used to depict clustering of symptoms at treatment end.