We propose a Bayesian regression model to study the time course expression profile of a virus-gene using data from micro-array experiments. Since the time course expression level of a virus gene in a cell is typically constantly zero initially, increasing for a while, and then decreasing, we consider regression model in which the mean function satisfies the above shape restriction. The prior is introduced through Bernstein polynomials. We note that Bernstein polynomial provides an excellent tool to incorporate geometric information into priors for Bayesian inference. We use the Metropolis-Hastings-Green algorithm to generate the posterior distribution and use the posterior mode as the estimate. This method is illustrated in simulation studies and analysis of real datasets. In this extended abstract, we include the expression profile of one of the genes of Baculavirus, obtained from the Bayesian method of this work. In the talk, I will also discuss applications of the expression profiles in the study of the virus genome. c 2005 IEEE.
Date:
2005-08
Relation:
Emerging Information Technology Conference 2005. 2005 Aug;170-171(Article number 1544357).
