Abstract
BackgroundBioinformatics can be used to predict protein function, leading to an understanding of cellular activities, and equally-weighted protein-protein interactions (PPI) are normally used to predict such protein functions. The present study provides a weighting strategy for PPI to improve the prediction of protein functions. The weights are dependent on the local and global network topologies and the number of experimental verification methods. The proposed methods were applied to the yeast proteome and integrated with the neighbour counting method to predict the functions of unknown proteins.ResultsA new technique to weight interactions in the yeast proteome is presented. The weights are related to the network topology (local and global) and the number of identified methods, and the results revealed improvement in the sensitivity and specificity of prediction in terms of cellular role and cellular locations. This method (new weights) was compared with a method that utilises interactions with the same weight and it was shown to be superior.ConclusionsA new method for weighting the interactions in protein-protein interaction networks is presented. Experimental results concerning yeast proteins demonstrated that weighting interactions integrated with the neighbor counting method improved the sensitivity and specificity of prediction in terms of two functional categories: cellular role and cell locations.
Highlights
Bioinformatics can be used to predict protein function, leading to an understanding of cellular activities, and -weighted protein-protein interactions (PPI) are normally used to predict such protein functions
In the first three Figures, the relationship between sensitivity and specificity was implemented for biochemical function, cell location and cellular role, respectively
The majority of methods concerning the estimation of protein functions through protein-protein interactions (PPI) use the same weights for all interactions
Summary
Bioinformatics can be used to predict protein function, leading to an understanding of cellular activities, and -weighted protein-protein interactions (PPI) are normally used to predict such protein functions. Other techniques to predict protein functions including analyzing gene expression patterns [1,2], phylogenetic profiles [3,4,5], protein sequences [6,7] and protein domains [8,9] have been utilised, but these technologies have high error rates, leading to the use of integrated multi-sources [10,11]. Researchers have introduced various techniques to determine the probability of protein function prediction using information extracted from PPI. Results from these trials have been promising, but they do not address effective problems including function correlation [12,13,14], network topology and strength of interaction
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