  We discuss probabilistic methods for predicting protein functions from
protein-protein interaction networks. Previous work based on Markov Randon
Fields is extended and compared to a general machine-learning theoretic
approach. Using actual protein interaction networks for yeast from the MIPS
database and GO-SLIM function assignments, we compare the predictions of the
different probabilistic methods and of a standard support vector machine. It
turns out that, with the currently available networks, the simple methods based
on counting frequencies perform as well as the more sophisticated approaches.
