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

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dc.creatorBest, Christoph
dc.creatorZimmer, Ralf
dc.creatorApostolakis, Joannis
dc.date2005-03-12
dc.date.accessioned2026-07-07T05:59:09Z
dc.date.available2026-07-07T05:59:09Z
dc.descriptionWe 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.
dc.description11 pages, 3 figures. Paper presented at the German Conference on Bioinformatics, 2004, Oct 4-6, Bielefeld, Germany
dc.identifierhttps://arxiv.org/abs/q-bio/0503018
dc.identifierhttp://arxiv.org/abs/q-bio/0503018
dc.identifierin: R. Giegerich, J. Stoye (eds.), German Conference on Bioinformatics 2004, Lecture Notes in Informatics, Ges. f. Informatik, Bonn, Germany, 2004
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/88574
dc.subjectMolecular Networks
dc.titleProbabilistic methods for predicting protein functions in protein-protein interaction networks
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