Discovering Local Structure in Gene Expression Data: The Order-Preserving Submatrix Problem

By A. Ben-Dor, Benny Chor, Richard M. Karp, Zohar Yakhini. Annual International Conference on Research in Computational Molecular Biology, 2002

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Finding large average submatrices in high dimensional data (2009, methodology): “(2002)]; the rows of U have equal [Ben-Dor et al. (2003)] or approximately equal [Liu, Yang and Wang (2004)] rank statistics; and all elements of U are above a given threshold [Prelic et al.”
De Novo Discovery of Mutated Driver Pathways in Cancer (2011, background): “Other variations, such as finding subsets of genes that preserve order of expression (Ben-Dor et al., 2003) or that cover many patients (Ulitsky et al.”
Biclustering algorithms for biological data analysis: a survey (2004, neutral): “Note that this approach is similar to the one followed by Ben-Dor et al. [2].”
Discovering statistically significant biclusters in gene expression data (2002, methodology): “Cheng and Church (2000) were the first to apply it to gene expression data.”
Biclustering in data mining (2008, methodology): “The idea of the algorithm from [45,46] is to increase c and d in the partial model until we get a good quality complete model.”
QUBIC: a qualitative biclustering algorithm for analyses of gene expression data (2009, background): “Recent studies suggest that this measure is useful only for identifying certain classes of co-expressed genes, but not adequate to detect other transcriptionally co-regulated genes (12–14).”