Skip Navigation



Bioinformatics Advance Access published online on May 30, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn253
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
24/14/1632    most recent
btn253v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Evers, L.
Right arrow Articles by Messow, C.-M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Evers, L.
Right arrow Articles by Messow, C.-M.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Sparse kernel methods for high-dimensional survival data

Ludger Evers a,* and Claudia-Martina Messow b

aDepartment of Mathematics, University of Bristol, University Walk, Bristol, BS8 1TW, United Kingdom, bInstitut für Medizinische Biometrie, Epidemiologie und Informatik, Johannes Gutenberg-Universität, 55101 Mainz, Germany

*To whom correspondence should be addressed. Dr. Ludger Evers, E-mail: l.evers{at}bris.ac.uk


  Abstract

Sparse kernel methods like support vector machines (SVM) have been applied with great success to classification and (standard) regression settings. Existing support vector classification and regression techniques however are not suitable for partly censored survival data, which are typically analysed using Cox's proportional hazards model. As the partial likelihood of the proportional hazards model only depends on the covariates through inner products, it can be "kernelised". The kernelised proportional hazards model however yields a solution that is dense, i.e. the solution depends on all observations. One of the key features of a support vector machine is that it yields a sparse solution, depending only on a small fraction of the training data. We propose two methods. One is based on a geometric idea, where — akin to support vector classification — the margin between the failed observation and the observations currently at risk is maximised. The other approach is based on obtaining a sparse model by adding observations one after another akin to the Import Vector Machine (IVM). Data examples studied suggest that both methods can outperform competing approaches.

Availability: Software is available under the GNU Public License as an R package and can be obtained from the first author's website http://www.maths.bris.ac.uk/~maxle/software.html.

Contact: l.evers{at}bris.ac.uk

Associate Editor: Dr. Limsoon Wong

Received on December 26, 2007; revised on May 8, 2008; accepted on May 29, 2008

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.