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Probabilistic methods for predicting protein functions in protein-protein interaction networks | | Christoph Best
; Ralf Zimmer
; Joannis Apostolakis
; | | Date: |
13 Mar 2005 | | Journal: | in: R. Giegerich, J. Stoye (eds.), German Conference on Bioinformatics 2004, Lecture Notes in Informatics, Ges. f. Informatik, Bonn, Germany, 2004 | | Subject: | Molecular Networks | q-bio.MN | | Affiliation: | Institute for Informatics, LMU, Munich, Germany | | Abstract: | 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. | | Source: | arXiv, q-bio.MN/0503018 | | Services: | Forum | Review | PDF | Favorites | |
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