Skip Navigation

Bioinformatics 2007 23(13):i459-i467; doi:10.1093/bioinformatics/btm218
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
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
Google Scholar
Right arrow Articles by Shi, Y.
Right arrow Articles by Bar-Joseph, Z.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Shi, Y.
Right arrow Articles by Bar-Joseph, Z.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Continuous hidden process model for time series expression experiments

Yanxin Shi 1, Michael Klustein 2, Itamar Simon 2, Tom Mitchell 1 and Ziv Bar-Joseph 1,*

1School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA and 2Department of Molecular Biology, Hebrew University Medical School, Jerusalem, 91120, Israel

*To whom correspondence should be addressed.


  Abstract

Motivation: When analyzing expression experiments, researchers are often interested in identifying the set of biological processes that are up-or down-regulated under the experimental condition studied. Current approaches, including clustering expression profiles and averaging the expression profiles of genes known to participate in specific processes, fail to provide an accurate estimate of the activity levels of many biological processes.

Results: We introduce a probabilistic continuous hidden process Model (CHPM) for time series expression data. CHPM can simultaneously determine the most probable assignment of genes to processes and the level of activation of these processes over time. To estimate model parameters, CHPM uses multiple time series datasets and incorporates prior biological knowledge. Applying CHPM to yeast expression data, we show that our algorithm produces more accurate functional assignments for genes compared to other expression analysis methods. The inferred process activity levels can be used to study the relationships between biological processes. We also report new biological experiments confirming some of the process activity levels predicted by CHPM.

Availability: A Java implementation is available at http:\\www.cs.cmu.edu\~yanxins\chpm

Contact: zivbj{at}cs.cmu.edu

Supplementary information: Supplementary data are available at Bioinformatics online.


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 Funct Genomic ProteomicHome page
F. Cordero, M. Botta, and R. A. Calogero
Microarray data analysis and mining approaches
Brief Funct Genomic Proteomic, January 22, 2008; (2008) elm034v1.
[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.