Computational discovery of gene modules and regulatory networks

By Ziv Bar-Joseph, G. Gerber, Tong Ihn Lee, Nicola J. Rinaldi, Jane Yoo, F. Robert, D. B. Gordon, E. Fraenkel, Tommi S. Jaakkola, Richard A. Young and 1 more. Nature Biotechnology, 2003

690 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/500b6cd6fd4dd69f47e35a4ad3df41ea303b24fb

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Citations in the corpus

Eigengene networks for studying the relationships between co-expression modules (2007, background): “Many possible approaches for defining network modules have been proposed in the literature [25-29].”
Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles (2007, background): “…machine-learning algorithms to identify cis -regulatory motifs or transcription factor target genes using a large set of expression arrays [2–18], genome-wide location analysis chromatin immunoprecipitation (ChIP-Chip) [19,20], or a combination of these and other high-throughput methods [21–26].”
From signatures to models: understanding cancer using microarrays (2005, background): “More recently, several studies 25–28 have attempted to identify how the set of cis-regulatory elements in a gene’s promoter governs its behavior and explains the observed expression pattern.”
Mapping of genetic and epigenetic regulatory networks using microarrays (2005, neutral): “Integration of microarray mRNA expression data and regulatory protein-binding map”
Graph theory and networks in Biology. (2006, neutral): “Other approaches to determining functional modules within transcriptional networks have been described in [25, 185].”
Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks (2005, neutral): “Terbinafine Ergosterol biosynthesis41 Erg1 Steroid metabolism (1, 10–14) ERG7 (4), ERG1 (5), ERG8 (11), ERG26 (13), UPC2 (17), ERG28 (18), ERG11 (20), DAP1 (33), HES1 (34), ATF2 (36), ERG5 (49)”
Gene regulatory network inference: Data integration in dynamic models - A review (2009, methodology): “Variants of this approach have been proposed for Boolean etworks (Birkmeier, 2006), linear difference equation models Yong-A-Poi, 2008; Koczan et al., 2008) and non-linear differential quation models (Spieth et al., 2005). For instance, a linear differnce equation model can be inferred using prior k”
Evaluation of different biological data and computational classification methods for use in protein interaction prediction (2006, methodology): “For example, gene expression is a consequence of protein– DNA binding, and so these two datasets are dependent.(7)”