How complete are current yeast and human protein-interaction networks?

By G. T. Hart, A. Ramani, Edward M. Marcotte. 2006

441 citations recorded by the source corpus.

We estimate the full yeast protein-protein interaction network to contain 37,800-75,500 interactions and the human network 154,000-369,000, but owing to a high false-positive rate, current maps are roughly only 50% and 10% complete, respectively. Paradoxically, releasing raw, unfiltered assay data might help separate true from false interactions.

Subfields: Molecular Biology

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In Molecular Biology

Citations in the corpus

Estimating the size of the human interactome (2008, background): “Unlike the previous approach of Hart et al. (24), who implicitly assume that interactions do not occur between surveyed proteins and those not yet surveyed, our estimate deals with missing data in a coherent and statistically meaningful manner; the route taken by Grigoriev (23) can be understood as ”
A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome (2015, background): “In principle, this discrepancy could be either a mere consequence of incomplete Interactome and gene-disease association data [5,10,26], or reflect an inherent fundamental difference between disease and topological modules.”
Network-based prediction of protein interactions (2018, background): “[6] Hart GT, Ramani AK & Marcotte EM, How complete are current yeast and human protein-interaction networks.”
ConsensusPathDB—a database for integrating human functional interaction networks (2008, background): “While for several model organisms like Saccharomyces cerevisiae (5) and Caenorhabditis elegans (6), such comprehensive functional interaction networks are available, the larger part of the human interactome remains undiscovered (7).”
Walking the interactome for prioritization of candidate disease genes. (2008, background): “30 The choice of data source to use for proteome analyses essentially amounts to a choice between coverage and accuracy.”
Computational approaches for detecting protein complexes from protein interaction networks: a survey (2010, unknown): “rates [95-97]”
Data Mining Techniques for the Life Sciences (2010, background): “The quality of high-throughput experiments has been long criticized with high false-positive rates of 50–70% (57).”
Network-based prediction of protein function (2007, background): “The currently available protein interaction data are known to be both noisy (von Mering et al , 2002) and partial (Hart et al , 2006).”
Where Have All the Interactions Gone? Estimating the Coverage of Two-Hybrid Protein Interaction Maps (2007, methodology): “A recent review points out the challenges in estimating false-positive rates, false-negative rates, and completion to full coverage of protein interaction networks [16].”