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Literature DB >> 20508635

Partitioning biological data with transitivity clustering.

Tobias Wittkop, Dorothea Emig, Sita Lange, Sven Rahmann, Mario Albrecht, John H Morris, Sebastian Böcker, Jens Stoye, Jan Baumbach.

Abstract

Mesh：

Year: 2010 PMID： 20508635 DOI： 10.1038/nmeth0610-419

Source DB: PubMed Journal: Nat Methods ISSN： 1548-7091 Impact factor: 28.547

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Authors: Tobias Wittkop; Jan Baumbach; Francisco P Lobo; Sven Rahmann
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Authors: Chris Lauber; Alexander E Gorbalenya
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2. Improving the quality of protein similarity network clustering algorithms using the network edge weight distribution.

Authors: Leonard Apeltsin; John H Morris; Patricia C Babbitt; Thomas E Ferrin
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3. Abundant toxin-related genes in the genomes of beneficial symbionts from deep-sea hydrothermal vent mussels.

Authors: Lizbeth Sayavedra; Manuel Kleiner; Ruby Ponnudurai; Silke Wetzel; Eric Pelletier; Valerie Barbe; Nori Satoh; Eiichi Shoguchi; Dennis Fink; Corinna Breusing; Thorsten Bh Reusch; Philip Rosenstiel; Markus B Schilhabel; Dörte Becher; Thomas Schweder; Stephanie Markert; Nicole Dubilier; Jillian M Petersen
Journal: Elife Date: 2015-09-15 Impact factor: 8.140

Partitioning biological data with transitivity clustering.

1. An efficient algorithm for large-scale detection of protein families.

2. Integration of biological networks and gene expression data using Cytoscape.

3. Large scale clustering of protein sequences with FORCE -A layout based heuristic for weighted cluster editing.

1. Partitioning the genetic diversity of a virus family: approach and evaluation through a case study of picornaviruses.

2. Improving the quality of protein similarity network clustering algorithms using the network edge weight distribution.

3. Abundant toxin-related genes in the genomes of beneficial symbionts from deep-sea hydrothermal vent mussels.

4. Comparing the performance of biomedical clustering methods.

5. Bi-Force: large-scale bicluster editing and its application to gene expression data biclustering.

6. FlowerNet: a gene expression correlation network for anther and pollen development.

7. Comprehensive cluster analysis with Transitivity Clustering.

8. BiCluE - Exact and heuristic algorithms for weighted bi-cluster editing of biomedical data.

9. Guiding biomedical clustering with ClustEval.

10. Finding approximate gene clusters with Gecko 3.