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  Protein Functional Class Prediction With a Combined Graph

Shin, H., Tsuda, K., & Schölkopf, B. (2008). Protein Functional Class Prediction With a Combined Graph. Expert Systems with Applications, 36(2,2), 3284-3292. doi:10.1016/j.eswa.2008.01.006.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CA2D-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-316E-B
Genre: Journal Article

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 Creators:
Shin, H, Author              
Tsuda, K1, 2, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: In bioinformatics, there exist multiple descriptions of graphs for the same set of genes or proteins. For instance, in yeast systems, graph edges can represent different relationships such as protein–protein interactions, genetic interactions, or co-participation in a protein complex, etc. Relying on similarities between nodes, each graph can be used independently for prediction of protein function. However, since different graphs contain partly independent and partly complementary information about the problem at hand, one can enhance the total information extracted by combining all graphs. In this paper, we propose a method for integrating multiple graphs within a framework of semi-supervised learning. The method alternates between minimizing the objective function with respect to network output and with respect to combining weights. We apply the method to the task of protein functional class prediction in yeast. The proposed method performs significantly better than the same algorithm trained on any singl e graph.

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 Dates: 2008-03
 Publication Status: Published in print
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 Identifiers: DOI: 10.1016/j.eswa.2008.01.006
BibTex Citekey: 5086
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Title: Expert Systems with Applications
Source Genre: Journal
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Pages: - Volume / Issue: 36 (2,2) Sequence Number: - Start / End Page: 3284 - 3292 Identifier: ISSN: 0957-4174