Transcriptional Regulatory Networks in Saccharomyces cerevisiae

By L. Kraal. 2009

865 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fa26a55ec872f03388343c7bbde36f19d6abc429

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Citations in the corpus

Using graph theory to analyze biological networks (2011, methodology): “Data collection, data integration and analysis techniques give now the possibility to study gene regulatory networks in a larger scale [26].”
Sparse partial least squares regression for simultaneous dimension reduction and variable selection (2010, background): “…which measures m rna levels at every 7 minutes for 119 minutes with a total of 18 measurements covering two cell cycle periods. c h ip -chip data of Lee et al. (2002) contains the binding information of 106 tf s which elucidate how yeast transcriptional regulators bind to promoter sequences of…”
Graph theory and networks in Biology. (2006, background): “This phenomenon has now been observed in metabolic, genetic and protein interaction networks.”
Centrality Analysis Methods for Biological Networks and Their Application to Gene Regulatory Networks (2008, background): “For example, the regulatory chain motif class, as in the example above, consists of a set of chains of three or more regulators in which one regulator regulates another regulator, which in turn regulates a third one and so forth (Lee et al. (2002)).”
Partial Correlation Estimation by Joint Sparse Regression Models (2008, background): “For example, experiments on model organisms like yeast or bacteria suggest that the transcriptional regulatory networks have modular structures (Lee et al. 2002).”
Harnessing naturally randomized transcription to infer regulatory relationships among genes (2007, background): “However, this goal is complicated by the fact that RNA levels, protein levels, phenotypes, and environmental conditions may all affect one another [6-10], creating intractable sources of confounding.”
Discovering Motifs in Ranked Lists of DNA Sequences (2007, methodology): “We applied DRIM to S. cerevisiae genome-wide location data reported in Harbison et al. [25] and Lee et al. [28].”
Parameter Estimation and Model Selection in Computational Biology (2010, background): “the genome of yeast, as reported in [4].”
GeneCodis: interpreting gene lists through enrichment analysis and integration of diverse biological information (2009, methodology): “In addition, we have used results from chip-on-chip experiments (15) to annotate genes in S.”
GeNGe: systematic generation of gene regulatory networks (2009, unknown): “Different types of such motifs have been experimentally identified by Lee et al. (2002): single-input motif (SIM), feed-forward loop (FFL), multi-component loop (MCL), multi-input motif (MIM), regular chain (RC), and auto-regulation (AR).”
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].”