Abstract
A new method for constructing gene networks from microarray time-series gene expression data is proposed in the context of Bayesian network approach. An essential point of Bayesian network modeling is the construction of the conditional distribution of each random variable. When estimating the conditional distributions from gene expression data, a common problem is that gene expression data contain multiple missing values. Unfortunately, many methods for constructing conditional distributions require a complete gene expression value and may lose effectiveness even with a few missing value. Additionally, they treat microarray time-series gene expression data as static data, although time can be an important factor that affects the gene expression levels. We overcome these difficulties by using the method of functional data analysis. The proposed network construction method consists of two stages. Firstly, discrete microarray time-series gene expression values are expressed as a continuous curve of time. To account for the time dependency of gene expression measurements and the noisy nature of the microarray data, P-spline nonlinear regression models are utilized. After this preprocessing step, the conditional distribution of each random variable is constructed based on functional linear regression models. The effectiveness of the proposed method is investigated through Monte Carlo simulations and the analysis of Saccharomyces cerevisiae gene expression data.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |
| Editors | J.A. Lopez, E. Benfenati, W. Dubitzky |
| Pages | 69-83 |
| Number of pages | 15 |
| Volume | 3303 |
| Publication status | Published - 2004 |
| Externally published | Yes |
| Event | International Symposium KELSI 2004: Knowledge Exploration in Life Science Informatics - Milan, Italy Duration: 2004 Nov 25 → 2004 Nov 26 |
Other
| Other | International Symposium KELSI 2004: Knowledge Exploration in Life Science Informatics |
|---|---|
| Country | Italy |
| City | Milan |
| Period | 04/11/25 → 04/11/26 |
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Keywords
- Bayesian networks
- Functional data analysis
- P-spline
- Smoothing
- Time-series gene expression data
ASJC Scopus subject areas
- Hardware and Architecture
Cite this
Functional data analysis of the dynamics of gene regulatory networks. / Ando, Tomohiro; Imoto, Seiya; Miyano, Satoru.
Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). ed. / J.A. Lopez; E. Benfenati; W. Dubitzky. Vol. 3303 2004. p. 69-83.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Functional data analysis of the dynamics of gene regulatory networks
AU - Ando, Tomohiro
AU - Imoto, Seiya
AU - Miyano, Satoru
PY - 2004
Y1 - 2004
N2 - A new method for constructing gene networks from microarray time-series gene expression data is proposed in the context of Bayesian network approach. An essential point of Bayesian network modeling is the construction of the conditional distribution of each random variable. When estimating the conditional distributions from gene expression data, a common problem is that gene expression data contain multiple missing values. Unfortunately, many methods for constructing conditional distributions require a complete gene expression value and may lose effectiveness even with a few missing value. Additionally, they treat microarray time-series gene expression data as static data, although time can be an important factor that affects the gene expression levels. We overcome these difficulties by using the method of functional data analysis. The proposed network construction method consists of two stages. Firstly, discrete microarray time-series gene expression values are expressed as a continuous curve of time. To account for the time dependency of gene expression measurements and the noisy nature of the microarray data, P-spline nonlinear regression models are utilized. After this preprocessing step, the conditional distribution of each random variable is constructed based on functional linear regression models. The effectiveness of the proposed method is investigated through Monte Carlo simulations and the analysis of Saccharomyces cerevisiae gene expression data.
AB - A new method for constructing gene networks from microarray time-series gene expression data is proposed in the context of Bayesian network approach. An essential point of Bayesian network modeling is the construction of the conditional distribution of each random variable. When estimating the conditional distributions from gene expression data, a common problem is that gene expression data contain multiple missing values. Unfortunately, many methods for constructing conditional distributions require a complete gene expression value and may lose effectiveness even with a few missing value. Additionally, they treat microarray time-series gene expression data as static data, although time can be an important factor that affects the gene expression levels. We overcome these difficulties by using the method of functional data analysis. The proposed network construction method consists of two stages. Firstly, discrete microarray time-series gene expression values are expressed as a continuous curve of time. To account for the time dependency of gene expression measurements and the noisy nature of the microarray data, P-spline nonlinear regression models are utilized. After this preprocessing step, the conditional distribution of each random variable is constructed based on functional linear regression models. The effectiveness of the proposed method is investigated through Monte Carlo simulations and the analysis of Saccharomyces cerevisiae gene expression data.
KW - Bayesian networks
KW - Functional data analysis
KW - P-spline
KW - Smoothing
KW - Time-series gene expression data
UR - http://www.scopus.com/inward/record.url?scp=22944434182&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=22944434182&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:22944434182
VL - 3303
SP - 69
EP - 83
BT - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
A2 - Lopez, J.A.
A2 - Benfenati, E.
A2 - Dubitzky, W.
ER -