A mixture model-based approach to the clustering of microarray expression data

By Geoffrey J. McLachlan, Richard Bean, David Peel. Bioinform., 2002

604 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/97ffbe88f79c1f4b5314da1a2d233da35d192d53

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Molecular Biology

Citations in the corpus

Penalized Model-Based Clustering with Application to Variable Selection (2007, background): “, 1998) and cancer subtype discovery (Golub et al., 1999; Ghosh and Chinnaiyan, 2002; McLachlan et al., 2002; Yeung et al., 2001).”
Model-based Methods of Classification: Using the mclust Software in Chemometrics (2007, methodology): “Many methods have been proposed for discriminant analysis, and it is applicable in a wide variety of settings (see e.g., Ripley 1996; Duda, Hart, and Storck 2001; Hastie, Tibshirani, and Friedman 2001; McLachlan, Bean, and Peel 2002).”
Comparisons and validation of statistical clustering techniques for microarray gene expression data (2003, methodology): “Model-based clustering is another technique that has recently been used for grouping microarray data ( McLachlan et al., 2002 ).”
Cluster analysis for gene expression data: a survey (2004, methodology): “Model-based clustering approaches [21, 76, 23, 45] provide a statistical framework to model the cluster structure of gene expression data.”
Variable Selection for Model-Based Clustering (2006, methodology): “…clustering context, work on variable selection has been done by Dy and Brodley (2000), Lazzeroni and Owen (2002), Getz, Levine, and Domany (2000), McLachlan, Bean, and Peel (2002), McCallum, Nigam, and Ungar (2000), Brusco and Cradit (2001), Devaney and Ram (1997), Friedman and Meulman (2004),…”
Methods of Microarray Data Analysis III (2003, methodology): “There are other methods of grouping genes for prediction, such as model-based clustering of genes [McLachlan et al., 2002].”
A Framework for Feature Selection in Clustering (2010, background): “Proposals for overcoming this problem include the factor analyzer approach of McLachlan, Bean, and Peel (2002) and McLachlan, Peel, and Bean (2003), which assumes that the observations lie in a low-dimensional latent factor space.”
Clustering cancer gene expression data: a comparative study (2008, background): “In contrast, common limitations of new methods proposed by bioinfor-maticians include the requirement of using particular programming environments and the specification of a number of different parameters (e.g., [6-8]), which makes their implementation difficult for non-expert users.”