Linked Life Data

11928508

Source:http://linkedlifedata.com/resource/pubmed/id/11928508

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2002-4-3
pubmed:abstractText
We introduce a new sequence-similarity kernel, the spectrum kernel, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. Our kernel is conceptually simple and efficient to compute and, in experiments on the SCOP database, performs well in comparison with state-of-the-art methods for homology detection. Moreover, our method produces an SVM classifier that allows linear time classification of test sequences. Our experiments provide evidence that string-based kernels, in conjunction with SVMs, could offer a viable and computationally efficient alternative to other methods of protein classification and homology detection.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1793-5091
pubmed:author
pubmed:issnType
Print
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
564-75
pubmed:dateRevised
2007-9-12
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
The spectrum kernel: a string kernel for SVM protein classification.
pubmed:affiliation
Department of Computer Science, Columbia University, New York, NY 10027, USA. cleslie.noble@cs.columbia.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't