Skip Navigation

This Article
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow FREE Full Text (Screen PDF)
Right arrow Alert me when this article is cited
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 ISI Web of Science
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 arrow Search for citing articles in:
ISI Web of Science (31)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Zhou, X.
Right arrow Articles by Dougherty, E. R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Zhou, X.
Right arrow Articles by Dougherty, E. R.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Bioinformatics Vol. 19 no. 17 2003
pages 2302-2307
© 2003 Oxford University Press

Missing-value estimation using linear and non-linear regression with Bayesian gene selection

Xiaobo Zhou 1, Xiaodong Wang 2 and Edward R. Dougherty 1,3,*

1 Department of Electrical Engineering, Texas A&M University, College Station, TX 77843, USA, 2 Department of Electrical Engineering, Columbia University, New York, NY 10027, USA and 3 Department of Pathology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA

Received on January 17, 2003 ; revised on February 23, 2003 ; accepted on June 11, 2003

Motivation: Data from microarray experiments are usually in the form of large matrices of expression levels of genes under different experimental conditions. Owing to various reasons, there are frequently missing values. Estimating these missing values is important because they affect downstream analysis, such as clustering, classification and network design. Several methods of missing-value estimation are in use. The problem has two parts: (1) selection of genes for estimation and (2) design of an estimation rule.

Results: We propose Bayesian variable selection to obtain genes to be used for estimation, and employ both linear and nonlinear regression for the estimation rule itself. Fast implementation issues for these methods are discussed, including the use of QR decomposition for parameter estimation. The proposed methods are tested on data sets arising from hereditary breast cancer and small round blue-cell tumors. The results compare very favorably with currently used methods based on the normalized root-mean-square error.

Availability: The appendix is available from http://gspsnap.tamu.edu/gspweb/zxb/missing_zxb/ (user: gspweb; passwd: gsplab).

Contact: edward{at}ee.tamu.edu

* To whom correspondence should be addressed.


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
 BioinformaticsHome page
D. S. V. Wong, F. K. Wong, and G. R. Wood
A multi-stage approach to clustering and imputation of gene expression profiles
Bioinformatics, April 15, 2007; 23(8): 998 - 1005.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
D. Hua and Y. Lai
An ensemble approach to microarray data-based gene prioritization after missing value imputation
Bioinformatics, March 15, 2007; 23(6): 747 - 754.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
J. Tuikkala, L. Elo, O. S. Nevalainen, and T. Aittokallio
Improving missing value estimation in microarray data with gene ontology
Bioinformatics, March 1, 2006; 22(5): 566 - 572.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
I. Scheel, M. Aldrin, I. K. Glad, R. Sorum, H. Lyng, and A. Frigessi
The influence of missing value imputation on detection of differentially expressed genes from microarray data
Bioinformatics, December 1, 2005; 21(23): 4272 - 4279.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
R. Jornsten, H.-Y. Wang, W. J. Welsh, and M. Ouyang
DNA microarray data imputation and significance analysis of differential expression
Bioinformatics, November 15, 2005; 21(22): 4155 - 4161.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
M. Scholz, F. Kaplan, C. L. Guy, J. Kopka, and J. Selbig
Non-linear PCA: a missing data approach
Bioinformatics, October 15, 2005; 21(20): 3887 - 3895.
[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.