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

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

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Molecular Networks

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http://salesiana.dossiersoluciones.com/handle/123456789/88574

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/q-bio/0503018
 http://arxiv.org/abs/q-bio/0503018
 in: R. Giegerich, J. Stoye (eds.), German Conference on Bioinformatics 2004, Lecture Notes in Informatics, Ges. f. Informatik, Bonn, Germany, 2004

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arXiv