Discovering statistically significant biclusters in gene expression data

By Amos Tanay, Roded Sharan, Ron Shamir. Intelligent Systems in Molecular Biology, 2002

979 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f810c1b09c73150294d5acd7e6ad8192144e54fe

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In Molecular Biology

Citations in the corpus

Network-based prediction of protein function (2007, methodology): “The SAMBA algorithm seeks modules consisting of a subset of genes and a subset of properties, such that these subsets are densely connected in the graph.”
Nonsmooth nonnegative matrix factorization (nsNMF) (2006, background): “The practical advantages of such biclustering methods has been proven in fields such as gene expression data analysis [28], [29], [30], [31], [32], [33], [34], topic extraction from documents [35], [36], and biomedical applications [37], to mention only a few.”
A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data (2015, methodology): “The Statistical-Algorithmic Method for Bicluster Analysis (SAMBA) algorithm [96] is a biclustering framework that combines PPI and DNA binding information.”
The road to modularity (2007, neutral): “Methods used to reveal co-regulated target genes involve clustering genes on the basis of the similarities in gene expression change”
Finding large average submatrices in high dimensional data (2009, background): “Thus, coding of the residual matrix takes nm[CN − log2[1− klτ 2 nm ]] bits on average.”
Approximating the cut-norm via Grothendieck's inequality (2004, background): “While trying to identify groups of genes with statistically significant correlated behaviour across diverse experiments, it is desirable to solve a certain biclustering problem, see [27], [26].”
FABIA: factor analysis for bicluster acquisition (2010, background): “Received on December 23, 2009; revised on March 26, 2010; accepted on April 20, 2010”
Parallel stochastic gradient algorithms for large-scale matrix completion (2013, background): “To see that no lower value is achievable requires an appeal to semidefinite programming and is proven in Recht et al. [32].”