Predicting the functions of proteins in Protein-Protein Interaction networks from global information
Hossein Rahmani, Hendrik Blockeel, Andreas Bender;
JMLR W&CP 8:82-97, 2010.
In this work we present a novel approach to predict the function of
proteins in protein-protein interaction (PPI) 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 for protein
function prediction. We here introduce a protein description
formalism that also includes global information, namely information
that locates a protein relative to specific important proteins in the
network. We analyze the effect on function prediction accuracy of
selecting a different number of important proteins. With around 70
important proteins, even in large graphs, our method makes good and
stable predictions. Furthermore, we investigate whether our method
also classifies proteins accurately on more detailed function levels.
We examined up to five different function levels. The method is
benchmarked on four datasets where we found classification performance
according to F-measure values indeed improves by 9 percent over the
benchmark methods employed.