The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking
Open accessAbstract
Summary: Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery. Availability: ConsensusClusterPlus is open source software, written in R, under GPL-2, and available through the Bioconductor project (http://www.bioconductor.org/). Contact: mwilkers@med.unc.edu Supplementary Information: Supplementary data are available at Bioinformatics online.
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Monti, S., Tamayo, P., Mesirov, J., & Golub, T. (2003) · Machine Learning · doi:10.1023/a:1023949509487 · Cited by 2,116
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“Unsupervised consensus clustering using Consensus Cluster Plus (Wilkerson and Hayes, 2010) was performed on 10,165 tumors with 15,363 genes.”
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Self-citationLandscape of somatic mutations in 560 breast cancer whole-genome sequences
Nik-Zainal, S., Davies, H., Staaf, J., Ramakrishna, M., Glodzik, D., Zou, X., Martincorena, I., Alexandrov, L. B., Martin, S., Wedge, D. C., et al. (2016) · Nature · doi:10.1038/nature17676 · Cited by 2,218
Metadata
- Authors
- Wilkerson, M. D., & Hayes, D. N.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-04-28
- Volume
- 26
- Issue
- 12
- Pages
- 1572-1573
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2115462905
- Cited by
- 5,669
- References
- 1
- FWCI
- 3.43
- Type
- journal article
- Language
- en
- Open access
- hybrid
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