Gaining confidence in high-throughput protein interaction networks

By Joel S. Bader, Amitabha Chaudhuri, Jonathan Rothberg, J. Chant. Nature Biotechnology, 2004

494 citations recorded by the source corpus.

Subfields: Cell Biology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b6f8fd327a0116e187fb1399d28f24ddc2235426

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Citations in the corpus

Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks (2012, methodology): “The interactions were assigned confidence scores based on the experimental evidence available for each interaction using a logistic regression model adapted from [59].”
Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners (2007, methodology): “Different classification methods have been successfully applied to the prediction of protein and domain interactions [41–54].”
How complete are current yeast and human protein-interaction networks? (2006, background): “[24] by fitting the interaction set as a linear combination of true-positive (small scale interactions) and false-positive (random pairs) interactions. interaction assay error rates [20,22,37,41-45] and for scoring individual protein-protein interactions.”
A proteome-wide fission yeast interactome reveals network evolution principles from yeasts to human (2016, result): “Furthermore, although it has been speculated that Y2H interactions involving proteins with many interaction partners (hubs) could be of low quality (Bader et al., 2004), we found that the validation rate by PCA of hub interactions is the same as the overall PCA validation rate for FissionNet (Figure”
Protein networks in disease. (2008, methodology): “A first set of methods (Jansen et al. 2003; von Mering et al. 2003; Bader et al. 2004; Lee et al. 2004; Myers and Troyanskaya 2007) was developed to clean the noise from raw interaction measurements, leading to higherconfidence networks with quantitative measures of accuracy associated with each int”
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”
Computational approaches for detecting protein complexes from protein interaction networks: a survey (2010, unknown): “rates [95-97]”
Detecting functional modules in the yeast protein-protein interaction network (2006, unknown): “…in the protein– protein interaction dataset of Saccharomyces cerevisiae that were obtained by high-throughput techniques (Uetz et al., 2000; Ito et al., 2001; Ho et al., 2002), assigning each interaction a confidence score (von Mering et al., 2002; Bader et al., 2004; Patil and Nakamura, 2005).”
Deciphering Protein–Protein Interactions. Part I. Experimental Techniques and Databases (2007, methodology): “Several methods have been proposed for verification of protein interaction data [66,67,76,98,99], and some of them are described here.”
Online Predicted Human Interaction Database (2005, unknown): “, 2002; Sprinzak and Margalit, 2001), gene co-expression (Bader et al., 2004; Deane et al., 2002; Deng et al., 2003) and Gene Ontology (GO) terms (Bader et al.”
Clique Relaxations in Social Network Analysis: The Maximum k-Plex Problem (2011, background): “In particular, structures where interactions of proteins occur through a central protein, which are likely to be found in similar biological processes, can be identified [Bader et al., 2004].”