Bayesian dynamic multivariate models for inferring gene interaction networks.
Constructions of gene and protein dynamic network is a challenging and important problem in genomic research while estimating the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop Bayesian dynamic multivariate models to tackle this challenge for inferring the gene network profiles associated with diseases and treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian setting. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Bayesian approaches with various prior and hyper-prior models with MCMC algorithms are used to estimate the model parameters. We apply our models to multiple tissue polygenetic affymetrix data sets. Preliminary results show that the genomic dynamic behavior can be well captured by the proposed model.
Liang
Yulan
Y
Dept. of Biostat., State Univ. of New York, Buffalo, NY 14214, USA.
Kelemen
Arpad
A
D001499:Bayes Theorem; D003198:Computer Simulation; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D015999:Multivariate Analysis; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome