Estimating time-dependent gene networks from time series microarray data by dynamic linear models with Markov switching.
In gene network estimation from time series microarray data, dynamic models such as differential equations and dynamic Bayesian networks assume that the network structure is stable through all time points, while the real network might changes its structure depending on time, affection of some shocks and so on. If the true network structure underlying the data changes at certain points, the fitting of the usual dynamic linear models fails to estimate the structure of gene network and we cannot obtain efficient information from data. To solve this problem, we propose a dynamic linear model with Markov switching for estimating time-dependent gene network structure from time series gene expression data. Using our proposed method, the network structure between genes and its change points are automatically estimated. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle time series data.
Yoshida
Ryo
R
Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo, 103-8569, Japan. yoshidar@ism.ac.jp
Imoto
Seiya
S
Higuchi
Tomoyuki
T
D000465:Algorithms; D001185:Artificial Intelligence; D002453:Cell Cycle / Q000502:physiology*; D018797:Cell Cycle Proteins / Q000378:metabolism*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D016014:Linear Models; D008390:Markov Chains; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D013997:Time Factors
D018797:Cell Cycle Proteins; D029701:Saccharomyces cerevisiae Proteins