Analyzing yeast protein–protein interaction data obtained from different sources

By Gary D. Bader, Christopher W. V. Hogue. Nature Biotechnology, 2002

585 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/69958922a8837d70d412193aefe78cf343655e18

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Citations in the corpus

Interactome: gateway into systems biology. (2005, methodology): “Integrating other types of data (133), either data from alternative PPI approaches (101,134,135), or data from altogether different functional genomics approaches (17,120,129,136–138), or both (79,96,102), with interactome maps is the most effective way to assign function to uncharacterized proteins”
Evaluation of different biological data and computational classification methods for use in protein interaction prediction (2006, methodology): “These three tasks are the prediction of ( 1 ) physical interaction, (2) co-complex relationship, and (3) pathway co-membership.”
How complete are current yeast and human protein-interaction networks? (2006, background): “In addition, for the mass spectrometry interactions, one can consider the bait-prey interactions (the ‘spoke’ model [29]) as well as the prey-prey interactions (the ‘matrix’ model), with the latter typically of lower accuracy.”
Gaining confidence in high-throughput protein interaction networks (2004, background): “The intersection of high-throughput data sets with known interactions implies a total of 20,000–30,000 specific protein interactions in yeast, with the majority remaining to be discovere”
Functional topology in a network of protein interactions (2004, methodology): “k-core concept (Bader and Hogue, 2002)], Watts–Strogatz’s node neighborhood ‘cliquishiness’ (Watts and Strogatz, 1998) [e.”
BioGRID: a general repository for interaction datasets (2005, background): “Proteomic and functional genomics platform technologies now generate large datasets of protein and gen-etic interactions, but these datasets vary widely in coverage, data quality, annotation and availability (4,5).”
HINT: High-quality protein interactomes and their applications in understanding human disease (2012, background): “This is expected given that models to generate topologies of cocomplex networks tend to include several or all possible combinations [25].”
An automated method for finding molecular complexes in large protein interaction networks (2003, unknown): “Advantages of the spoke model are that it is biologically intuitive, biologists often represent their copurification results in this manner, and is about 3 times more accurate than the matrix model [31].”
PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine (2003, background): “If one assumes that all bait and target proteins identified in an immunoprecipitation interact with one another (matrix representation) the number of potential interactions increases significantly [30].”
The Biomolecular Interaction Network Database and related tools 2005 update (2004, methodology): “The BIND Index is recommended for researchers who prefer to work directly in SQL and contains the core information in BIND records including primary database identifiers, publications, non-redundant interactions, matrix and spoke models of BIND complexes (26), taxonomies, short labels and experiment”
Large-scale mapping of human protein–protein interactions by mass spectrometry (2007, methodology): “Although the accuracy of considering the matrix of all interactions is expected to be lower than when only considering bait–prey interactions (Bader and Hogue, 2002), clearly many valid interactions remain to be discovered from this broader approach.”