Best, ChristophZimmer, RalfApostolakis, JoannisGiegerich, RobertStoye, Jens2019-10-112019-10-1120043-88579-382-2https://dl.gi.de/handle/20.500.12116/28662We 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.enText/Conference Paper1617-5468