Improved functional prediction of proteins by learning kernel combinations in multilabel settings

Author:

Roth Volker,Fischer Bernd

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology

Reference21 articles.

1. Lanckriet G, Deng M, Cristianini N, Jordan M, Noble W: Kernel-based data fusion and its application to protein function prediction in yeast. Pacific Symposium on Biocomputing 2004, 300–311.

2. Sonnenburg S, Rätsch G, Schäfer C: A general and efficient multiple kernel learning algorithm. In NIPS 18. Edited by: Weiss Y, Schölkopf B, Platt J. MIT Press; 2006.

3. Bach F, Lanckriet G, Jordan M: Multiple kernel learning, conic duality, and the SMO algorithm. 21st Intern Conference on Machine Learning 2004.

4. Crammer K, Keshet J, Singer Y: Kernel design using boosting. In NIPS 15. MIT Press; 2002:537–544.

5. Centeno TP, Lawrence N: Optimising kernel parameters and regularisation coefficients for non-linear discriminant analysis. Journal of Machine Learning Research 2006., 7(455–49):

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