Literature DB >> 15201188

The CRASSS plug-in for integrating annotation data with hierarchical clustering results.

Eugen C Buehler1, Jeffrey R Sachs, Kui Shao, Ansuman Bagchi, Lyle H Ungar.   

Abstract

We describe an algorithm for finding the most statistically significant non-overlapping subtrees of a hierarchical clustering of gene expression data with respect to a set of secondary data labels on genes. The method is implemented as a Java plug-in for a commercial gene expression analysis program (GeneSpring).

Mesh：

Year:  2004        PMID： 15201188     DOI： 10.1093/bioinformatics/bth362

Source DB:  PubMed          Journal:  Bioinformatics        ISSN： 1367-4803            Impact factor:   6.937


  5 in total

1.  Seeing the forest for the trees: using the Gene Ontology to restructure hierarchical clustering.

Authors:  Dikla Dotan-Cohen; Simon Kasif; Avraham A Melkman
Journal:  Bioinformatics       Date:  2009-06-03       Impact factor: 6.937

2.  Trans-omics analysis of insulin action reveals a cell growth subnetwork which co-regulates anabolic processes.

Authors:  Akira Terakawa; Yanhui Hu; Toshiya Kokaji; Katsuyuki Yugi; Keigo Morita; Satoshi Ohno; Yifei Pan; Yunfan Bai; Andrey A Parkhitko; Xiaochun Ni; John M Asara; Martha L Bulyk; Norbert Perrimon; Shinya Kuroda
Journal:  iScience       Date:  2022-04-08

3.  Improving clustering with metabolic pathway data.

Authors:  Diego H Milone; Georgina Stegmayer; Mariana López; Laura Kamenetzky; Fernando Carrari
Journal:  BMC Bioinformatics       Date:  2014-04-10       Impact factor: 3.169

4.  CLEAN: CLustering Enrichment ANalysis.

Authors:  Johannes M Freudenberg; Vineet K Joshi; Zhen Hu; Mario Medvedovic
Journal:  BMC Bioinformatics       Date:  2009-07-29       Impact factor: 3.169

5.  A general strategy to determine the congruence between a hierarchical and a non-hierarchical classification.

Authors:  Antonio Marco; Ignacio Marín
Journal:  BMC Bioinformatics       Date:  2007-11-15       Impact factor: 3.169
  5 in total

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