Comparative assessment of large-scale data sets of protein–protein interactions

By C. von Mering, R. Krause, Berend Snel, Michael J. Cornell, S. Oliver, Stanley Fields, Peer Bork. Nature, 2002

2,514 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8c48222c90bbeff0bcddad3cea75e4581167a190

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In Molecular Biology

Citations in the corpus

A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference (2010, background): “The other is the protein interaction network of von Mering et al. (2002).”
Associating Genes and Protein Complexes with Disease via Network Propagation (2010, background): “However, those were shown to produce high rates of false negatives and false positives [32].”
The Database of Interacting Proteins: 2004 update (2004, background): “As a result, a number of attempts has been made to assess the quality of the high-throughput data (6,12,13).”
Rapid Subfunctionalization Accompanied by Prolonged and Substantial Neofunctionalization in Duplicate Gene Evolution (2005, methodology): “Yeast protein-protein interaction data were obtained from RESULTS MIPS and from the high-confidence subset of interaction data Analysis of yeast protein-protein interaction data: Tocompiled by von Mering et al. (2002).”
Protein interaction networks from yeast to human. (2004, background): “see [38]) that it can be used to systematically survey genomes for experimentally uncharacterized systems as a guide for future experiments.”
Functional and evolutionary inference in gene networks: does topology matter? (2006, methodology): “Data for protein-network connectivity were obtained from von Mering et al. (2002).”
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.”
Protein-protein interaction prediction via Collective Matrix Factorization (2010, methodology): “Several methods help to infer protein interactions based on the conservation of gene neighborhood, conservation of gene order, gene fusion events, and the co-evolution of interacting protein pair sequences [15][ 16 ].”
Pairwise Global Alignment of Protein Interaction Networks by Matching Neighborhood Topology (2007, methodology): “These techniques have also been supplemented by promising new computational approaches [ 27 , 24, 23, 26, 17, 29, 9] to PPI prediction, resulting in an explosive growth in available PPI data.”
Why Do Hubs in the Yeast Protein Interaction Network Tend To Be Essential: Reexamining the Connection between the Network Topology and Essentiality (2008, background): “Moreover, due to its binary nature, the Y2H technique may completely miss interactions in complexes that require cooperative binding [34].”
Systematic image-driven analysis of the spatial Drosophila embryonic expression landscape (2010, background): “Currently, a high false positive rate is associated with the yeast two-hybrid data, which limits the value of such comparisons (von Mering et al , 2002).”
Modeling interactome: scale-free or geometric? (2004, positive): “In addition, the shortest path distribution and the frequencies of 3–15-node cycles in the high-conﬁdence fruitﬂy PPI network have been shown to differ from those of randomly rewired networks which preserve the same degree distribution as the original PPI network (Giot et al ., 2003).”
MPact: the MIPS protein interaction resource on yeast (2005, methodology): “We describe MPact, a manually annotated protein interaction database in yeast as a reference for the experimental and theoretical work to elucidate the characteristics of cellular protein interaction networks ( 3 ,5,7,9).”