Jump to ContentJump to Main Navigation
Semi-Supervised Learning$
Users without a subscription are not able to see the full content.

Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien

Print publication date: 2006

Print ISBN-13: 9780262033589

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262033589.001.0001

Show Summary Details
Page of

PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 18 September 2021

Semi-Supervised Protein Classification Using Cluster Kernels

Semi-Supervised Protein Classification Using Cluster Kernels

Chapter:
(p.342) (p.343) 19 Semi-Supervised Protein Classification Using Cluster Kernels
Source:
Semi-Supervised Learning
Author(s):

Weston Jason

Leslie Christina

Ie Eugene

Noble William Stafford

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262033589.003.0019

This chapter describes an experimental study of large-scale semi-supervised learning for the problem of protein classification. The protein classification problem, a central problem in computational biology, is to predict the structural class of a protein given its amino acid sequence. Such a classification helps biologists to understand the function of a protein. Building an accurate protein classification system, as with many tasks, depends critically upon choosing a good representation of the input sequences of amino acids. Early work using string kernels with support vector machines (SVMs) for protein classification achieved state-of-the-art classification performance. However, such representations are based only on labeled data—examples with known three-dimensional (3D) structures, organized into structural classes-while in practice, unlabeled data are far more plentiful.

Keywords:   semi-supervised learning, protein classification problem, computational biology, amino acid sequence, input sequences, string kernels, support vector machines, SVMs, labeled data, unlabeled data

MIT Press Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs, and if you can't find the answer there, please contact us.