False positive reduction in protein-protein interaction predictions using gene ontology annotations

Abstract

Background: Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated.

Fast Inference for the Latent Space Network Model Using a Case-Control Approximate Likelihood

“…These false positive links can yield misleading scientific hypotheses and lead to costly and unproductive biological validation experiments. Hence, there is great interest in finding ways to identify and remove these false positive links (Mahdavi and Lin 2007; Kuchaiev et al 2009). …”

“…We would suspect that those links in the data with very low fitted probabilities are probably false positive links. Mahdavi and Lin (2007) used Gene Ontology (GO) annotations to reduce false positive protein-protein interactions (PPI) pairs resulting from computational predictions. The key idea is that interacting proteins are likely to share GO slim terms.…”

Protein-protein interaction prediction by combined analysis of genomic and conservation information

“…Furthermore, in vitro methods can usually recognize permanent PPIs and therefore, cannot detect all PPIs (Zahiri et al, 2013b). Generally, network reconstruction approaches are very successful in unveiling regulatory relationships and other interesting biological phenomena, but they may lead to a large number of false positive interactions (Mahdavi and Lin, 2007). It has been shown that improvement of performance in such methods can be achieved by some sorts of computational methods (Rhodes et al, 2005), hence the incentive for the emergence of such methods to predict PPIs.…”

“…It has been shown that improvement of performance in such methods can be achieved by some sorts of computational methods (Rhodes et al, 2005), hence the incentive for the emergence of such methods to predict PPIs. The computational methods are regarded as a complement to the in vitro methods; in fact, combination of experimental and computational methods can outperform PPI predicted using each method, because of the reduction in the rate of false-positive generation (Mahdavi and Lin, 2007;Shoemaker and Panchenko, 2007a). There are different classes of the PPI prediction methods:…”

Protein-Protein Interactions and Disease

“…Gene Ontology (GO) cellular component annotations from PlasmoDB (Aurrecoechea et al, 2009), a comprehensive Plasmodium resource, were used to prune the unified PPI dataset using the approach of Mahdavi & Lin (2007). In the case of PPI involving parasite proteins, only those proteins that were annotated to be present on the parasite surface or were reported to be released during the relevant stage of the parasite were considered (Lyon et al, 1986).…”