Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data

By Eran Segal, M. Shapira, Aviv Regev, Dana Pe'er, David Botstein, Daphne Koller, Nir Friedman. Nature Genetics, 2003

1,882 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8bf6c7eef879565d2416a730e26c24536bf25a34

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Citations in the corpus

Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles (2007, background): “Pioneering efforts to identify regulatory interactions on a genome scale have used machine-learning algorithms to identify cis-regulatory motifs or transcription factor target genes using a large set of expression arrays [2–18], genomewide location analysis chromatin immunoprecipitation (ChIP-Chip) ”
Robustness of Cellular Functions (2004, positive): “tradeoffs, (Segal et al., 2003; Ihmels et al., 2004), and integrated networks (Tanay et al., 2004).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “This is one reason, why combination of various methods (or the use of iterative approaches) may outperform individual methodologies (Liang et al., 1998a; Akutsu et al., 1999; Ideker et al., 2000; Kholodenko et al., 2002; Yeung et al., 2002; Segal et al., 2003; Tegnér et al., 2003; Friedman, 2004; Te”
The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo (2006, positive): “Biclustering was developed to address better the full complexity of finding co-regulated genes under multifactor con-trol by grouping genes on the basis of coherence under subsets of observed conditions [10,16-22].”
Densely interconnected transcriptional circuits control cell states in human hematopoiesis (2011, positive): “To dissect the architecture of the gene expression program, we used the Module Networks (Segal et al., 2003) algorithm (Experimental Procedures) to ﬁnd modules of strongly coexpressed genes and associate them with candidate regulatory programs that (computationally) predict their expression pattern.”
Global and Local Architecture of the Mammalian microRNA–Transcription Factor Regulatory Network (2007, background): “The combinatorial interactions between TFs have been explored [14,15] as well as the global level properties of the transcription regulatory network [16].”
Advantages and limitations of current network inference methods (2010, neutral): “By exploiting modularity, LeMoNe and related method”
Computational solutions for omics data (2013, methodology): “For example, CONEXIC compares gene expression data in cancer tissue and normal tissue by extending the causal graphical model 76 , which connects gene sets with edges to represent their interactions, to elucidate dysregulated gene modules from cancer genomic data 88 .”
The mathematics of tanning (2009, positive): “The module network (Additional file 4) was computed using four Cdc25 and four Elu data (Oliva Elu 1&2 not used) with Genomica [ 64 ].”
Graph theory and networks in Biology. (2006, background): “Further approaches to the determination of functional modules within biological networks have been described in [128, 183].”
Eigengene networks for studying the relationships between co-expression modules (2007, methodology): “Module detection using hierarchical clustering Many possible approaches for defining network modules have been proposed in the literature [25-29].”