Bayesian forecasting of temporal gene expression by using an autoregressive panel data approach
“Bayesian forecasting of temporal gene expression by using an autoregressive panel data approach”, vol. 15, p. -, 2016.,
We propose and evaluate a novel approach for forecasting gene expression over non-observed times in longitudinal trials under a Bayesian viewpoint. One of the aims is to cluster genes that share similar expression patterns over time and then use this similarity to predict relative expression at time points of interest. Expression values of 106 genes expressed during the cell cycle of Saccharomyces cerevisiae were used and genes were partitioned into five distinct clusters of sizes 33, 32, 21, 16, and 4.