PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semi-Supervised Protein Classification using Cluster Kernels
Jason Weston, Christina Leslie, Dengyong Zhou, Andre Elisseeff and William Stafford Noble
In: NIPS 2003, Vancouver, Canada(2004).


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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:479
Deposited By:Dengyong Zhou
Deposited On:23 December 2004