ArticleFrontiers in genetics2016
Detection of Significant Groups in Hierarchical Clustering by Resampling.
Article in Frontiers in genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 31 citations in OpenAlex.
- Machine Learning and Statistical Shape Modelling Methodologies to Assess Vascular Morphology before and after Aortic Valve Replacement.Journal of clinical medicine · 2024Article
- DendroX: multi-level multi-cluster selection in dendrograms.BMC genomics · 2024Article
- Early Biomarker Signatures in Surgical Sepsis.The Journal of surgical research · 2022Article
- Remapping of Adult-Born Neuron Activity during Fear Memory Consolidation in Mice.International journal of molecular sciences · 2021Article
- Patterns of multi-domain cognitive aging in participants of the Long Life Family Study.GeroScience · 2020Article
- Biomarker signatures of sickle cell disease severity.Blood cells, molecules & diseases · 2018Article
- Inflammatory signatures distinguish metabolic health in African American women with obesity.PloS one · 2018Article
- Biomarker signatures of aging.Aging cell · 2017Article
- Sickle cell anemia: hierarchical cluster analysis and clinical profile in a cohort in Brazil.Hematology, transfusion and cell therapyArticle
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Hierarchical clustering is a simple and reproducible technique to rearrange data of multiple variables and sample units and visualize possible groups in the data. Despite the name, hierarchical clustering does not provide clusters automatically, and "tree-cutting" procedures are often used to identify subgroups in the data by cutting the dendrogram that represents the similarities among groups used in the agglomerative procedure. We introduce a resampling-based technique that can be used to identify cut-points of a dendrogram with a significance level based on a reference distribution for the heights of the branch points. The evaluation on synthetic data shows that the technique is robust in a variety of situations. An example with real biomarker data from the Long Life Family Study shows the usefulness of the method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.