Semi-supervised protein classification using cluster kernels.
BACKGROUND
Building an accurate protein classification system depends critically upon choosing a good representation of the 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--whereas in practice, unlabeled data are far more plentiful.


RESULTS
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 and at the same time achieving far greater computational efficiency.


BACKGROUND
Source code is available at www.kyb.tuebingen.mpg.de/bs/people/weston/semiprot. The Spider matlab package is available at www.kyb.tuebingen.mpg.de/bs/people/spider.


BACKGROUND
www.kyb.tuebingen.mpg.de/bs/people/weston/semiprot.
Weston
Jason
J
NEC Research Institute, 4 Independence Way, Princeton, NJ 08540, USA. jasonw@nec-labs.com
Leslie
Christina
C
Ie
Eugene
E
Zhou
Dengyong
D
Elisseeff
Andre
A
Noble
William Stafford
WS
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000032:analysis / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
D011506:Proteins