Skip Navigation



Bioinformatics Advance Access published online on July 26, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl393
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
22/19/2388    most recent
btl393v1
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 Pan, W.
Right arrow Articles by Hebbel, R. P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Pan, W.
Right arrow Articles by Hebbel, R. P.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received April 21, 2006
Revised July 10, 2006
Accepted July 11, 2006

Article

Semi-supervised learning via penalized mixture model with application to microarray sample classification

Wei Pan 1 *, Xiaotong Shen 2, Aixiang Jiang 3, and Robert P. Hebbel 4

1 Division of Biostatistics, School of Public Health, University of Minnesota
2 School of Statistics, University of Minnesota
3 Department of Biostatistics, Vanderbilt University
4 Vascular Biology Center and Division of Hematology-Oncology-Transplantation, University of Minnesota Medical School

* To whom correspondence should be addressed.
Wei Pan, E-mail: weip{at}biostat.umn.edu


  Abstract

Motivation: It is biologically interesting to address whether human blood outgrowth endothelial cells (BOECs) belong to or are closer to large vessel endothelial cells (LVECs) or microvascular endothelial cells (MVECs) based on global expression profiling. An earlier analysis using a hierarchical clustering and a small set of genes suggested that BOECs seemed to be closer to MVECs. By taking advantage of the two known classes, LVEC and MVEC, while allowing BOEC samples to belong to either of the two classes or to form their own new class, we take a semi-supervised learning approach; for high-dimensional data as encountered here, we propose a penalized mixture model with a weighted L1 penalty to realize automatic feature selection while fitting the model.

Results: We applied our penalized mixture model to a combined dataset containing 27 BOEC, 28 LVEC and 25 MVEC samples. Analysis results indicated that the BOEC samples appeared to form their own new class. A simulation study confirmed that, compared to the standard mixture model with or without initial variable selection, the penalized mixture model performed much better in identifying relevant genes and forming corresponding clusters. The penalized mixture model seems to be promising for high-dimensional data with the capability of novel class discovery and automatic feature selection.

Associate Editor: John Quackenbush
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
 Brief BioinformHome page
S. Ma and J. Huang
Penalized feature selection and classification in bioinformatics
Brief Bioinform, September 1, 2008; 9(5): 392 - 403.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
G. C. Tseng
Penalized and weighted K-means for clustering with scattered objects and prior information in high-throughput biological data
Bioinformatics, September 1, 2007; 23(17): 2247 - 2255.
[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.