Probabilistic methods for predicting protein functions in   protein-protein interaction networks
  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. 
Christoph Best, Ralf Zimmer, Joannis Apostolakis (Institute for   Informatics, LMU, Munich, Germany)