Probabilistic methods for predicting protein functions in protein-protein interaction networks

Details Probabilistic methods for predicting protein functions in protein-protein interaction networks Probabilistic methods for predicting protein functions in protein-protein interaction networks

2005-03-12

arxiv.org/abs/q-bio/0503018v1

in: R. Giegerich, J. Stoye (eds.), German Conference on Bioinformatics 2004, Lecture Notes in Informatics, Ges. f. Informatik,

Biology

Quantitative Biology

Molecular Networks

11 pages, 3 figures. Paper presented at the German Conference on Bioinformatics, 2004, Oct 4-6, Bielefeld, Germany

Scientific paper

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.

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