TY - GEN
T1 - Probabilistic methods for predicting protein functions in protein-protein interaction networks
AU - Best, Christoph
AU - Zimmer, Ralf
AU - Apostolakis, Joannis
N1 - Publisher Copyright:
© 2004 Gesellschaft fur Informatik (GI). All rights reserved.
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84925053964
M3 - Conference contribution
AN - SCOPUS:84925053964
T3 - Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
SP - 159
EP - 168
BT - Proceedings of the German Conference on Bioinformatics, GCB 2004
A2 - Giegerich, Robert
A2 - Stoye, Jens
PB - Gesellschaft fur Informatik (GI)
T2 - 2004 German Conference on Bioinformatics, GCB 2004
Y2 - 4 October 2004 through 6 October 2004
ER -