Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data

Monti, Stefano; Tamayo, Pablo; Mesirov, Jill; Golub, Todd

doi:10.1023/a:1023949509487

Cited by 2,116Gene expression and cancer classificationAdvanced Clustering Algorithms ResearchBayesian Methods and Mixture Models

The DOI matches its Crossref record; 48 of the 49 references it cites were found in a registry or an index.

Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data

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Monti, Stefano, Tamayo, Pablo, Mesirov, Jill, Golub, Todd·2003·Machine Learning

DOI 10.1023/a:1023949509487

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Consensus clustering identified five major proteomic subtypes in the tumor cohort, with 15, 9, 25, 11, and 19 cases in subtypes A through E.
“Using the Consensus Clustering24 method (Supplementary Methods 9.1–9.2, Extended Data Fig. 9), we identified five major proteomic subtypes in this tumor cohort, with 15, 9, 25, 11, and 19 cases in subtypes A through E, respectively (Fig. 4a–b).”
From Proteogenomic characterization of human colon and rectal cancer (2014) · cites this paper as [24]
We previously identified three robust and reproducible transcriptomic sub-groups of KRAS-mutant LUAD by applying non-negative matrix factorization consensus clustering to RNASeq data from 68 tumors from TCGA.
“We previously identified three robust and reproducible transcriptomic sub-groups of KRAS-mutant LUAD by applying non-negative matrix factorization consensus clustering81–83 to RNASeq data from 68 tumors from The Cancer Genome Atlas (TCGA) dataset67.”
From Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy (2019) · cites this paper as [83]

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Authors: Monti, S., Tamayo, P., Mesirov, J., & Golub, T.
Affiliations: Massachusetts Institute of Technology (US)
ORCID: Monti, Stefano
Tamayo, Pablo
Mesirov, Jill
Golub, Todd
Venue: Machine Learning
Publisher: Springer Science and Business Media LLC
Published: 2003-07
Volume: 52
Issue: 1-2
Pages: 91-118
ISSN: 0885-6125 (ISSN-L)
1573-0565
DOI: 10.1023/a:1023949509487
OpenAlex: W1548779692
Cited by: 2,116
References: 49
Citations by year: 2026: 134 · 2025: 113 · 2024: 155 · 2023: 133 · 2022: 139 · 2021: 192 · 2020: 123 · 2019: 112 · 2018: 120 · 2017: 100 · 2016: 85 · 2015: 94 · 2014: 78 · 2013: 77 · 2012: 80
FWCI: 8.98
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Machine Learning (publishedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
Keywords: consensus clustering, gene expression microarrays, cluster validation, cluster stability, resampling, cluster visualization, optimal number of clusters, K-means clustering, self-organizing maps, Bayesian clustering, sensitivity to initial conditions

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Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data

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