Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data
Joint Authors
Tian, Li-Ping
Liu, Li-Zhi
Wu, Fang-Xiang
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-01-02
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Microarray technology has produced a huge body of time-course gene expression data and will continue to produce more.
Such gene expression data has been proved useful in genomic disease diagnosis and drug design.
The challenge is how to uncover useful information from such data by proper analysis methods such as significance analysis and clustering analysis.
Many statistic-based significance analysis methods and distance/correlation-based clustering analysis methods have been applied to time-course expression data.
However, these techniques are unable to account for the dynamics of such data.
It is the dynamics that characterizes such data and that should be considered in analysis of such data.
In this paper, we employ a nonlinear model to analyse time-course gene expression data.
We firstly develop an efficient method for estimating the parameters in the nonlinear model.
Then we utilize this model to perform the significance analysis of individually differentially expressed genes and clustering analysis of a set of gene expression profiles.
The verification with two synthetic datasets shows that our developed significance analysis method and cluster analysis method outperform some existing methods.
The application to one real-life biological dataset illustrates that the analysis results of our developed methods are in agreement with the existing results.
American Psychological Association (APA)
Tian, Li-Ping& Liu, Li-Zhi& Wu, Fang-Xiang. 2014. Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1049189
Modern Language Association (MLA)
Tian, Li-Ping…[et al.]. Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data. The Scientific World Journal No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1049189
American Medical Association (AMA)
Tian, Li-Ping& Liu, Li-Zhi& Wu, Fang-Xiang. Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1049189
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049189