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Semi-Supervised Protein Classification using Cluster Kernels


Conference Paper


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.

Author(s): Weston, J. and Leslie, C. and Zhou, D. and Elisseeff, A. and Noble, WS.
Book Title: Advances in Neural Information Processing Systems 16
Journal: Advances in Neural Information Processing Systems
Pages: 595-602
Year: 2004
Month: June
Day: 0
Editors: Thrun, S., L.K. Saul, B. Sch{\"o}lkopf
Publisher: MIT Press

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

Event Name: Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 0
Institution: MPI, Tuebingen, Germany
ISBN: 0-262-20152-6
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Semi-Supervised Protein Classification using Cluster Kernels},
  author = {Weston, J. and Leslie, C. and Zhou, D. and Elisseeff, A. and Noble, WS.},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 16},
  pages = {595-602},
  editors = {Thrun, S., L.K. Saul, B. Sch{\"o}lkopf},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  institution = {MPI, Tuebingen, Germany},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jun,
  year = {2004},
  month_numeric = {6}