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Protein Functional Class Prediction with a Combined Graph

2004

Conference Paper

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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 single graph.

Author(s): Shin, H. and Tsuda, K. and Schölkopf, B.
Journal: Proceedings of the Korean Data Mining Conference
Pages: 200-219
Year: 2004
Day: 0

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Proceedings of the Korean Data Mining Conference

Digital: 0
Institution: Max Planck Institute for Biological Cybernetics, Tuebingen
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{3054,
  title = {Protein Functional Class Prediction with a Combined Graph},
  author = {Shin, H. and Tsuda, K. and Sch{\"o}lkopf, B.},
  journal = {Proceedings of the Korean Data Mining Conference},
  pages = {200-219},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics, Tuebingen},
  school = {Biologische Kybernetik},
  year = {2004}
}