Since interactions among proteins are of central
importance for virtually every process in living cells, their
information improves our understanding of diseases and can
provide the basis for new therapeutic approaches. The study of
protein function prediction based on protein-protein interactions (PPI) is one the most important issues in the field of bioinformatics since it is crucial for the understanding of cell
activities. In this paper, a comparative study of different methods for protein function prediction based on PPI is presented.
Five selected methods including the neighbor counting, Chisquare, Markov random field, Prodistin, and weighted interactions methods are applied to yeast proteome and their prediction performance is compared. Results revealed variant differences in the sensitivity and specificity of prediction for the
functional category: cellular role. The weighted interactions
method showed the best sensitivity values, while the Chisquare method resulted in the worst sensitivity/specificity
values