Predicting the functions of proteins in PPI networks from global information

In this work we present a novel approach to predict the function of proteins in protein-protein interaction networks. We classify existing approaches into inductive and transductive approaches, and into local and global approaches. As of yet, among the group of inductive approaches, only local ones have been proposed. We here introduce a protein description formalism that also includes global information, namely information that locates a protein relative to specific other proteins in the network. The method is benchmarked on four datasets and we found that on these datasets classification according to precision and AUC values indeed improves over the benchmark methods employed.

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