Skip Navigation



Bioinformatics Advance Access published online on June 16, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti544
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
21/16/3377    most recent
bti544v1
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 Liu, H.
Right arrow Articles by Wong, L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Liu, H.
Right arrow Articles by Wong, L.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received January 4, 2005
Revised May 12, 2005
Accepted June 14, 2005

Article

Use of extreme patient samples for outcome prediction from gene expression data

Huiqing Liu 1*, Jinyan Li 1, and Limsoon Wong 1

1 Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613

* To whom correspondence should be addressed.
Huiqing Liu, E-mail: huiqing{at}i2r.a-star.edu.sg


  Abstract

Motivation: Patient outcome prediction using microarray technologies is an important application in bioinformatics. Based on patients' genotypic microarray data, predictions are made to estimate patients' survival time and their risk of tumor metastasis or recurrence. So, accurate prediction can potentially help to provide better treatment for patients.

Results: We present a new computational method for patient outcome prediction. In the training phase of this method, we make use of two types of extreme patient samples: short-term survivors who got an unfavorable outcome within a short period and long-term survivors who were maintaining a favorable outcome after a long follow-up time. These extreme training samples yield a clear platform for us to identify relevant genes whose expression is closely related to the outcome. The selected extreme samples and the relevant genes are then integrated by a support vector machine to build a prediction model, by which each validation sample is assigned a risk score that falls into one of special pre-defined risk groups. We apply this method to several public data sets. In most cases, patients in high and low risk groups stratified by our method have clearly distinguishable outcome status as seen in their Kaplan-Meier curves. We also show that the idea of selecting only extreme patient samples for training is effective for improving the prediction accuracy when different gene selection methods are used.

Supplementary information: http://research.i2r.a-star.edu.sg/huiqing/supplementaldata/survival/survival.html.


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


This article has been cited by other articles:


Home page
 JCOHome page
A. M. Glas, L. Knoops, L. Delahaye, M. J. Kersten, R. E. Kibbelaar, L. A. Wessels, R. van Laar, J. H. J.M. van Krieken, J. W. Baars, J. Raemaekers, et al.
Gene-Expression and Immunohistochemical Study of Specific T-Cell Subsets and Accessory Cell Types in the Transformation and Prognosis of Follicular Lymphoma
J. Clin. Oncol., February 1, 2007; 25(4): 390 - 398.
[Abstract] [Full Text] [PDF]


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.