Probabilistic methods for predicting protein functions in protein-protein interaction networks
| dc.contributor.author | Best, Christoph | |
| dc.contributor.author | Zimmer, Ralf | |
| dc.contributor.author | Apostolakis, Joannis | |
| dc.contributor.editor | Giegerich, Robert | |
| dc.contributor.editor | Stoye, Jens | |
| dc.date.accessioned | 2019-10-11T11:32:38Z | |
| dc.date.available | 2019-10-11T11:32:38Z | |
| dc.date.issued | 2004 | |
| dc.description.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. | en |
| dc.identifier.isbn | 3-88579-382-2 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/28662 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | German Conference on Bioinformatics 2004, GCB 2004 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-53 | |
| dc.title | Probabilistic methods for predicting protein functions in protein-protein interaction networks | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 168 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 159 | |
| gi.conference.date | October 4-6, 2004 | |
| gi.conference.location | Bielefeld | |
| gi.conference.sessiontitle | Regular Research Papers |
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