Skip Navigation


Bioinformatics Advance Access originally published online on February 15, 2005
Bioinformatics 2005 21(9):2118-2122; doi:10.1093/bioinformatics/bti318
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
21/9/2118    most recent
bti318v1
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 (2)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Ji, Y.
Right arrow Articles by Coombes, K. R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ji, Y.
Right arrow Articles by Coombes, K. R.
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{at}oupjournals.org

Applications of beta-mixture models in bioinformatics

Yuan Ji 1,*, Chunlei Wu 2, Ping Liu 1, Jing Wang 1 and Kevin R. Coombes 1

1Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center Houston, TX 77030, USA
23M Pharmaceuticals St Paul, Minnesota, USA

*To whom correspondence should be addressed.

Summary: We propose a beta-mixture model approach to solve a variety of problems related to correlations of gene-expression levels. For example, in meta-analyses of microarray gene-expression datasets, a threshold value of correlation coefficients for gene-expression levels is used to decide whether gene-expression levels are strongly correlated across studies. Ad hoc threshold values such as 0.5 are often used. In this paper, we use a beta-mixture model approach to divide the correlation coefficients into several populations so that the large correlation coefficients can be identified. Another important application of the proposed method is in finding co-expressed genes. Two examples are provided to illustrate both applications. Through our analysis, we also discover that the popular model selection criteria BIC and AIC are not suitable for the beta-mixture model. To determine the number of components in the mixture model, we suggest an alternative criterion, ICL–BIC, which is shown to perform better in selecting the correct mixture model.

Contact: yuanji{at}mdanderson.org

Supplementary information: http://odin.mdacc.tmc.edu/~yuanj/highcorgeneanno.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
 BioinformaticsHome page
J. Zhang, Y. Ji, and L. Zhang
Extracting three-way gene interactions from microarray data
Bioinformatics, November 1, 2007; 23(21): 2903 - 2909.
[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.