Revealing modular organization in the yeast transcriptional network

By Jan Ihmels, G. Friedlander, Sven Bergmann, O. Sarig, Yaniv Ziv, Naama Barkai. Nature Genetics, 2002

771 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5e7362fdc706297561ab32aa2ce0113d9d0435ee

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Citations in the corpus

Similarities and Differences in Genome-Wide Expression Data of Six Organisms (2003, methodology): “For example, it was demonstrated that diverse datasets of genome-wide expression profiles can be applied for facilitating functional assignment of uncharacterized ORFs and for identification of cis-regulatory elements (Eisen et al. 1998; Kim et al. 2001; Ihmels et al. 2002).”
Robustness of Cellular Functions (2004, positive): “However, it is essential to note that the role of feedback involves hard tradeoffs, (Segal et al., 2003; Ihmels et al., 2004), and integrated networks (Tanay et al.”
TRRUST: a reference database of human transcriptional regulatory interactions (2015, background): “Thus, target genes regulated by the same TF tend to be modular 13 , often comprising protein complexes or pathways.”
Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq (2017, methodology): “To refine the definition of these expression programs in the context of IDH-A, we used an approach similar to the signature algorithm (31).”
Advantages and limitations of current network inference methods (2010, methodology): “Query-driven module detection methods, on the other han”
Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. (2016, methodology): “We are motivated by two biological assumptions: (1) modularity (Costanzo et al., 2016; Ihmels et al., 2002), as we have shown for both the perturbations and the gene targets; and (2) sparsity, such that themajority of gene pairs (or higher order combinations) do not manifest genetic interactions (su”
Structure and evolution of transcriptional regulatory networks. (2004, neutral): “[13] added a different perspective by taking the experimental conditions into account when defining the gene clusters.”
Global analysis of protein expression in yeast (2003, background): “A recent analysis of mRNA expression profiles from ∼1,000 published microarray experiments allowed for the identification of 33 ‘modules’ of transcriptionally co-regulated gene”
Discovering molecular pathways from protein interaction and gene expression data (2003, background): “Recently, several approaches have been proposed that discover condition-speciﬁc groups from gene expression (Ihmels et al. , 2002; Segal et al. , 2001).”
Ribosome assembly in eukaryotes. (2003, background): “, 2001) YGR081C Pre-40S ND V YKL143W LTV1 YKL2 Pre-40S ND V (Ihmels et al., 2002) YOR056C NOB1 Pre-40S ND No/Nu L YOR145C Pre-40S ND Cy L (Rout et al.”
Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering (2002, methodology): “[11] have applied a heuristic algorithm to the analysis of yeast gene-expression data to identify over-lapping sets of genes whose expression is similar to known gene-expression patterns.”
Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks (2005, background): “By exploiting the insight that subtle but coordinated changes in expression can be detected by combining measurements across multiple members of a functional module, these focused studies have successfully found specific modules that are important in diabetes [12], aging [13], and cancer [10,11,14,1”
Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases (2018, methodology): “Likewise, methods for detection of overlapping modules (Ihmels et al., 2002) may also be assessed using the challenge framework, but will require development of novel scoring metrics that take module overlap into account.”
Computational prediction of transcription-factor binding site locations (2003, background): “kinds of transcription-factor binding-site data, together with mRNA expression analysis, protein-interaction databases and prior genetic and biochemical data in the literature, will allow the construction of more detailed connectivity maps of transcriptional regulatory networks [10,13,121-125].”
Defining transcription modules using large-scale gene expression data (2004, methodology): “The approach described in this paper complements the recurrent signature method we proposed recently (Ihmels et al., 2002), which provides an intuitive way for integrating external biological information, such as sequence information, functional annotation or protein–protein interactions.”