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

By Stefano Monti, Pablo Tamayo, Jill P. Mesirov, Todd R. Golub. Machine-mediated learning, 2003

2,095 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1f29553ecbaa388b6be3402bc7af28178f5e24ef

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Citations in the corpus

Unsupervised extraction of functional gene expression signatures in the bacterial pathogen Pseudomonas aeruginosa with eADAGE (2016, methodology): “To directly target stability, we introduce an unsupervised modeling procedure inspired by consensus clustering (Monti et al , 2003).”
Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms (2004, methodology): “The next section describes evaluation graphs, followed by a description of the L method.”
Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures (2019, methodology): “A consensus clustering algorithm (28) was applied to define the cluster of genes, and principal component analysis (PCA) was conducted.”
The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma (2018, methodology): “The K-value of 6 was selected according to the consensus cumulative distribution function, where K > 6 did not produce any appreciable increase in consensus (Monti et al., 2003; Wilkerson and Hayes, 2010).”
Ensemble learning: A survey (2018, methodology): “Consensus clustering has been shown to be very effective in discovering biological meaningful clusters in gene expression data (Kiselev et al., 2017; Monti et al., 2003; Verhaak et al., 2010), video shot segmentation (Chang, Lee, Hong, & Archibald, 2008; Zheng, Zhang, & Li, 2012), online event…”
Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors (2007, methodology): “Consensus clustering CC [20] was performed locally using Gene Pattern 1.”
Information theoretic measures for clusterings comparison: is a correction for chance necessary? (2009, background): “In an era where a huge number of clustering algorithms exist, the Consensus Clustering idea (Monti et al., 2003; Strehl & Ghosh, 2002; Yu et al., 2007) has recently received increasing interest.”
CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets (2017, neutral): “For more details about the meaning of this plot, the user can refer to the original description of the consensus clustering method (Monti et al., 2003).”
A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue (2007, background): “Above factors make the clustering or classification results susceptible to over-fitting and sometimes under-fitting problem [3,21,23,24].”
Cluster-wise assessment of cluster stability (2007, background): “Examples are Ben-Hur et al. (2002), Bryan (2004), Dudoit and Fridlyand (2002), Grün and Leisch (2004), Lange et al. (2004), Monti et al. (2001) and Tibshirani and Walther (2005).”
Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. (2010, methodology): “We selected the most variant probes on each platform and performed consensus clustering to identify GBM subgroups Cancer Cell 17, 510–522, May 18, 2010 ª2010 Elsevier Inc. 511 (Monti et al., 2003).”
Network-based stratification of tumor mutations (2013, methodology): “33 and used in previous TCGA publications 2,18,60 .”