Skip Navigation


Bioinformatics Advance Access originally published online on May 15, 2009
Bioinformatics 2009 25(14):1775-1781; doi:10.1093/bioinformatics/btp322
This Article
Right arrow Full Text
Right arrow Full Text (Print PDF)
Right arrow All Versions of this Article:
25/14/1775    most recent
btp322v1
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 Sohn, I.
Right arrow Articles by Park, C.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Sohn, I.
Right arrow Articles by Park, C.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Gradient lasso for Cox proportional hazards model

Insuk Sohn 1, Jinseog Kim 2, Sin-Ho Jung 1 and Changyi Park 3,*

1 Department of Biostatistics & Bioinformatics, Duke University, NC 27705, USA, 2 Department of Statistics and Information Science, Dongguk University, Gyeongju 780-714 and 3 Department of Statistics, University of Seoul, Seoul 130-743, Korea

* To whom correspondence should be addressed.


  Abstract

Motivation: There has been an increasing interest in expressing a survival phenotype (e.g. time to cancer recurrence or death) or its distribution in terms of a subset of the expression data of a subset of genes. Due to high dimensionality of gene expression data, however, there is a serious problem of collinearity in fitting a prediction model, e.g. Cox's proportional hazards model. To avoid the collinearity problem, several methods based on penalized Cox proportional hazards models have been proposed. However, those methods suffer from severe computational problems, such as slow or even failed convergence, because of high-dimensional matrix inversions required for model fitting. We propose to implement the penalized Cox regression with a lasso penalty via the gradient lasso algorithm that yields faster convergence to the global optimum than do other algorithms. Moreover the gradient lasso algorithm is guaranteed to converge to the optimum under mild regularity conditions. Hence, our gradient lasso algorithm can be a useful tool in developing a prediction model based on high-dimensional covariates including gene expression data.

Results: Results from simulation studies showed that the prediction model by gradient lasso recovers the prognostic genes. Also results from diffuse large B-cell lymphoma datasets and Norway/Stanford breast cancer dataset indicate that our method is very competitive compared with popular existing methods by Park and Hastie and Goeman in its computational time, prediction and selectivity.

Availability: R package glcoxph is available at http://datamining.dongguk.ac.kr/R/glcoxph.

Contact: park463{at}uos.ac.kr

Associate Editor: John Quackenbush

Received on February 9, 2009; revised on May 7, 2009; accepted on May 12, 2009

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.