Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae

By Hui Ge, George M. Church, Marc Vidal. Nature Genetics, 2001

640 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5d87397679cb2d2265d85707af5ddf9a494d23c4

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Citations in the corpus

An experimentally derived confidence score for binary protein-protein interactions (2008, neutral): “Early quality assessments used the strength of a correlation with indirect secondary data, such as coexpression or functional annotation, mostly to measure the overall data set qualit”
Evidence for dynamically organized modularity in the yeast protein–protein interaction network (2004, background): “would be uncorrelated in mRNA expression with their interaction partner”
Coexpression analysis of human genes across many microarray data sets. (2004, background): “It has been shown in a number of studies that coexpression is correlated with functional relationships, such as physical interaction between the encoded proteins, though coexpression does not necessarily imply a causal relationship among transcript levels (Eisen et al. 1998; Ge et al. 2001; Jansen e”
The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics (2007, background): “While there were several studies addressing correlations among them, they focused largely on local properties [32].”
Strategies for aggregating gene expression data: The collapseRows R function (2011, background): “For example, in the case of transcriptional networks, genes showing positive correlation are more likely than uncorrelated or negatively correlated genes to have known protein-protein interactions [2-4].”
Comparative assessment of large-scale data sets of protein–protein interactions (2002, background): “In many cases, however, such functional associations do take the form of physical bindin”
Protein interaction networks from yeast to human. (2004, background): “However, highquality subsets could often be chosen on the basis of additional criteria [15 ,16 ,38,39], such as the degree to which mRNAs of interacting proteins are co-expressed in microarray experiments [40,41], topological properties of the resulting networks [42,43], shared pathways or subcellul”
Protein interaction mapping: a Drosophila case study. (2005, background): “elegans (Ge et al. 2001; Li et al. 2004), proves to be even more powerful in assigning unannotated proteins to biological pathways.”
Discovering molecular pathways from protein interaction and gene expression data (2003, background): “The asssumption that many pathways exhibit both properties is supported by the work of Ge et al. (2001), that showed that genes with similar expression proﬁles are more likely to encode interacting proteins, and by the study of Jansen et al. (2002), which showed that the genes of experimentally…”
Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast (2003, methodology): “This type of analysis has been used to validate, for example, gene expression and protein-protein interaction data, 5 ; 6 ; 7 to validate protein-protein interactions predicted using ﬂve diﬁerent methods, 8 and to infer protein function.”
Online Predicted Human Interaction Database (2005, positive): “Several studies have suggested that gene co-expression provides evidence for protein interactions (Deane et al. , 2002; Ge et al. , 2001; Kemmeren et al. , 2002).”
Functional topology in a network of protein interactions (2004, background): “The integration of PPI data sets with annotation or gene expression data might prove to be a useful solution to the problem, as co-expression could enable prediction of subcomplexes within biological complexes (Ge et al., 2001) or to separate transient and stable complexes (Jansen et al.”
Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks (2006, result): “This is in agreement with the recent discovery that there is often a correlation between genes that are expressed together and proteins that bind to each other [ 27 ].”
Network-based prediction of protein function (2007, background): “As shown in both simple and complex organisms (Ge et al , 2001; Hahn et al , 2005), genes of interacting proteins tend to share similar expression patterns.”
Probabilistic model of the human protein-protein interaction network (2005, methodology): “Contingency tables detailing the intersection of predicted interactions with the GSP and GSN sets and the resultant likelihood ratios are provided online as Supplementary Tables 1–8 .”
MPact: the MIPS protein interaction resource on yeast (2005, methodology): “It was shown that genes with similar expression proﬁles are more likely to encode interacting proteins, thus describing a subset of functional modules, named ‘party hubs’, in contrast to ‘date hubs’ which consists of interacting proteins not synchronized by co-regulation (5–9).”
Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae*S (2007, background): “Finally, it is known that genes encoding physically interacting proteins are more likely to have similar expression profiles (10, 23, 27, 28), and so we examined the distribution of Pearson correlation coefficients between expression patterns of interacting pairs over a set of 1000 previously publis”