de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Semi-Supervised Protein Classification using Cluster Kernels

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84311

Weston,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84330

Leslie C, Zhou,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Weston, J., Leslie C, Zhou, D., Elisseeff, A., & Noble, W. (2004). Semi-Supervised Protein Classification using Cluster Kernels. Advances in Neural Information Processing Systems, 595-602.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D8FD-C
Abstract
A key issue in supervised protein classification is the representation of input sequences of amino acids. Recent work using string kernels for protein data has achieved state-of-the-art classification performance. However, such representations are based only on labeled data --- examples with known 3D structures, organized into structural classes --- while in practice, unlabeled data is far more plentiful. In this work, we develop simple and scalable cluster kernel techniques for incorporating unlabeled data into the representation of protein sequences. We show that our methods greatly improve the classification performance of string kernels and outperform standard approaches for using unlabeled data, such as adding close homologs of the positive examples to the training data. We achieve equal or superior performance to previously presented cluster kernel methods while achieving far greater computational efficiency.