DMCA
Stochastic dynamic modeling of short gene expression time series data (2008)
| Venue: | IEEE Trans. NanoBiosci |
| Citations: | 22 - 9 self |
BibTeX
@ARTICLE{Wang08stochasticdynamic,
author = {Zidong Wang and Senior Member and Fuwen Yang and Senior Member and Danielw. C. Ho and Senior Member and Stephen Swift and Allan Tucker and Xiaohui Liu},
title = {Stochastic dynamic modeling of short gene expression time series data},
journal = {IEEE Trans. NanoBiosci},
year = {2008},
pages = {44--55}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract—In this paper, the expectation maximization (EM) algorithm is applied for modeling the gene regulatory network from gene time-series data. The gene regulatory network is viewed as a stochastic dynamic model, which consists of the noisy gene measurement from microarray and the gene regulation first-order autoregressive (AR) stochastic dynamic process. By using the EM algorithm, both the model parameters and the actual values of the gene expression levels can be identified simultaneously. Moreover, the algorithm can deal with the sparse parameter identification and the noisy data in an efficient way. It is also shown that the EM algorithm can handle the microarrary gene expression data with large number of variables but a small number of observations. The gene expression stochastic dynamic models for four real-world gene expression data sets are constructed to demonstrate the advantages of the introduced algorithm. Several indices are proposed to evaluate the models of inferred gene regulatory networks, and the relevant biological properties are discussed. Index Terms—Clustering, DNA microarray technology, expectation maximization (EM) algorithm, gene expression, modeling, time-series data. I.
Keyphrases
stochastic dynamic modeling gene regulatory network em algorithm expectation maximization index term clustering relevant biological property time-series data inferred gene regulatory network real-world gene expression data set large number several index microarrary gene expression data noisy gene measurement gene expression stochastic dynamic model introduced algorithm efficient way gene regulation first-order autoregressive noisy data stochastic dynamic model gene expression model parameter sparse parameter identification gene time-series data small number stochastic dynamic process dna microarray technology gene expression level actual value
