Comparisons and validation of statistical clustering techniques for microarray gene expression data

By Susmita Datta, Somnath Datta. Bioinform., 2003

410 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a6cd66298e0a34d7355d24f317ded58e32d9912c

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Citations in the corpus

AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data (2018, background): “It is also a common approach to use the correlation distance in the case of pattern recognition in microarray gene expression data (Datta and Datta, 2003).”
Microarray data analysis: from disarray to consolidation and consensus (2006, background): “We believe that unsupervised classification is overused; first, little information is available about the absolute validity or relative merits of clustering procedure”
Computational cluster validation in post-genomic data analysis (2005, background): “Datta and Datta (2003) extend this approach to the computation of a ﬁgure of merit by means of different internal validity indices—speciﬁcally, one measure of pair-wise co-assignment, one of cluster separation and one of cluster compactness.”
Clustering cancer gene expression data: a comparative study (2008, methodology): “The shortcomings of hierarchical methods is noticeable, as it has been the case in the analyses developed in the context of clustering genes [31,32].”
RNA secondary structure prediction by centroids in a Boltzmann weighted ensemble. (2005, methodology): “For hierarchical clustering of structures, we employ Diana (Kaufman and Rousseeuw 1990), a top-down divisive method that has performed well in other contexts (Datta and Datta 2003).”
How does gene expression clustering work? (2005, background): “(cid:127) k -means and SOM outperform hierarchical clustering 8–10 .”
A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data (2012, methodology): “To further quantitatively describe these properties, a clustering analysis based on the hierarchical cluster algorithm [59,60] was conducted (The whole clustering analysis information are provided in Supporting Informaton S14, S15, S16).”