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We began analyzing https://academic.oup.com/bioinformatics/article/24/5/719/200751, but it redirected us to https://academic.oup.com/bioinformatics/article/24/5/719/200751. The analysis below is for the second page.

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Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R | Bioinformatics | Oxford Academic
Description:
Abstract. Summary: Hierarchical clustering is a widely used method for detecting clusters in genomic data. Clusters are defined by cutting branches off the

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ScholarlyArticle:
      context:https://schema.org
      id:https://academic.oup.com/bioinformatics/article/24/5/719/200751
      name:Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R
      datePublished:2007-11-16
      isPartOf:
         id:https://academic.oup.com/bioinformatics/issue/24/5
         type:PublicationIssue
         issueNumber:5
         datePublished:2008-03-01
         isPartOf:
            id:https://academic.oup.com/bioinformatics/bioinformatics
            type:Periodical
            name:Bioinformatics
            issn:
               1367-4811
      url:https://dx.doi.org/10.1093/bioinformatics/btm563
      inLanguage:en
      copyrightHolder:Oxford University Press
      copyrightYear:2025
      publisher:Oxford University Press
      author:
            name:Langfelder, Peter
            affiliation:Department of Human Genetics, University of California at Los Angeles, CA 90095-7088 and Rosetta Inpharmatics-Merck Research Laboratories, Seattle, WA, USA
            type:Person
            name:Zhang, Bin
            affiliation:Department of Human Genetics, University of California at Los Angeles, CA 90095-7088 and Rosetta Inpharmatics-Merck Research Laboratories, Seattle, WA, USA
            type:Person
            name:Horvath, Steve
            affiliation:Department of Human Genetics, University of California at Los Angeles, CA 90095-7088 and Rosetta Inpharmatics-Merck Research Laboratories, Seattle, WA, USA
            type:Person
      description:Abstract. Summary: Hierarchical clustering is a widely used method for detecting clusters in genomic data. Clusters are defined by cutting branches off the
      pageStart:719
      pageEnd:720
      siteName:OUP Academic
      thumbnailURL:https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bioinformatics/24/5/10.1093_bioinformatics_btm563/3/m_bioinformatics_24_5_719_f2.jpeg?Expires=1813928759&Signature=Lvf7mkJvRHYL86KsnNDHEaIVKeZ4Wm-961pViI1L5xdAotguUPuNMIGfmexNPnaiKgFZOEVBjGDjjfI4EWQfbSntTnS3RnNE9oWj-8MkMd6Y2pDyIhAUGSPVICuGdWvJupSaMeRAXj~JZkwwzc8-KakDPtmBFm6BfimbMvr3pYsHdv5UjyxDg4m6zEp0ZHQiiVxeEpHXyrp6hMX1IbC-Tr8ItOhGuDJe37MzO-smjBdb2bJsmAzuKozwYmqYVVP7dhJJrOcf-WHJ4tECNoSBTU6wMdgV6I~04rj7JlfdO-Mwy7~yrVzz5J0F-9LGkPSigU188ZJYyid-CZjRQWioSA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA
      headline:Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R
      image:https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bioinformatics/24/5/10.1093_bioinformatics_btm563/3/m_bioinformatics_24_5_719_f2.jpeg?Expires=1813928759&Signature=Lvf7mkJvRHYL86KsnNDHEaIVKeZ4Wm-961pViI1L5xdAotguUPuNMIGfmexNPnaiKgFZOEVBjGDjjfI4EWQfbSntTnS3RnNE9oWj-8MkMd6Y2pDyIhAUGSPVICuGdWvJupSaMeRAXj~JZkwwzc8-KakDPtmBFm6BfimbMvr3pYsHdv5UjyxDg4m6zEp0ZHQiiVxeEpHXyrp6hMX1IbC-Tr8ItOhGuDJe37MzO-smjBdb2bJsmAzuKozwYmqYVVP7dhJJrOcf-WHJ4tECNoSBTU6wMdgV6I~04rj7JlfdO-Mwy7~yrVzz5J0F-9LGkPSigU188ZJYyid-CZjRQWioSA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA
      image:alt:(A) Average linkage hierarchical clustering using the Topological Overlap Matrix (Yip and Horvath, 2007) and the Dynamic Tree cut applied to the protein–protein interaction network of Drosophila (PPI data from BioGRID, www.thebiogrid.org). Module assignment is depicted by the row of color immediately below the dendrogram, with gray representing unassigned proteins. A functional enrichment analysis has shown that the clusters are significantly enriched with known gene ontologies (Dong and Horvath, 2007). Note that a fixed height cutoff would not be able to identify many of the shown clusters. (B) Hierarchical cluster tree and various cluster detection methods applied to a simulated gene expression data set. The color bands below the dendrogram show the cluster membership according to different clustering methods. The color gray is reserved for genes outside any proper cluster, i.e., the tree cut methods allow for unassigned objects. The first color band ‘Simulated’ shows the simulated true cluster membership; color bands ‘Dynamic Hybrid’ and ‘Dynamic Tree’ show the results of the proposed tree cutting methods; the color band ‘Static @ 0.92’ shows the results of the standard, constant height cut-off method at height 0.92. The height refers to the y axis of the dendrogram. The color band ‘PAM 11’ shows the results of k = 11 medoid clustering.
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      issueNumber:5
      datePublished:2008-03-01
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         issn:
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Periodical:
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      name:Bioinformatics
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Person:
      name:Langfelder, Peter
      affiliation:Department of Human Genetics, University of California at Los Angeles, CA 90095-7088 and Rosetta Inpharmatics-Merck Research Laboratories, Seattle, WA, USA
      name:Zhang, Bin
      affiliation:Department of Human Genetics, University of California at Los Angeles, CA 90095-7088 and Rosetta Inpharmatics-Merck Research Laboratories, Seattle, WA, USA
      name:Horvath, Steve
      affiliation:Department of Human Genetics, University of California at Los Angeles, CA 90095-7088 and Rosetta Inpharmatics-Merck Research Laboratories, Seattle, WA, USA

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