ABSTRACT
The paper discusses a variety of recently developed Bayesian techniques for detection of differentially expressed genes on the basis of time series microarray data. These techniques have different strengths and weaknesses and are constructed for different kinds of experimental designs. However, the Bayesian formulations, which the above described methodologies utilize, allow one to explicitly use prior information that biologists may provide. The methods successfully deal with various technical difficulties that arise in microarray time-course experiments such as a large number of genes, a small number of observations, non-uniform sampling intervals, missing or multiple data, and temporal dependence between observations for each gene.
