Bayesian Forecasting Of Temporal Gene Expression By Using An Autoregressive Panel Data Approach

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)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. After removing the last observed time point, the agreements of signals (upregulated or downregulated) considering the predicted expression level were 72.7, 81.3, 76.2, 68.8, and 50.0%, respectively, for each cluster. The percentage of credibility intervals that contained the true values of gene expression for a future time was ~90%. The methodology performed well, providing a valid forecast of gene expression values by fitting an autoregressive panel data model. This approach is easily implemented with other time-series models and when Poisson and negative binomial probability distributions are assumed for the gene expression data. © FUNPEC-RP.152CAPES, Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológico#APQ 00825, FAPEMIG, Fundação de Amparo à Pesquisa do Estado de Minas GeraisFUNARBE, Fundação Arthur BernardesCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Bayesian Analysis and Constraints on Kinematic Models from Union SNIa

The kinematic expansion history of the universe is investigated by using the 307 supernovae type Ia from the Union Compilation set. Three simple model parameterizations for the deceleration parameter (constant, linear and abrupt transition) and two different models that are explicitly parametrized by the cosmic jerk parameter (constant and variable) are considered. Likelihood and Bayesian analyses are employed to find best fit parameters and compare models among themselves and with the flat $\Lambda$CDM model. Analytical expressions and estimates for the deceleration and cosmic jerk parameters today ($q_0$ and $j_0$) and for the transition redshift ($z_t$) between a past phase of cosmic deceleration to a current phase of acceleration are given. All models characterize an accelerated expansion for the universe today and largely indicate that it was decelerating in the past, having a transition redshift around 0.5. The cosmic jerk is not strongly constrained by the present supernovae data. For the most realistic kinematic models the $1\sigma$ confidence limits imply the following ranges of values: $q_0\in[-0.96,-0.46]$, $j_0\in[-3.2,-0.3]$ and $z_t\in[0.36,0.84]$, which are compatible with the $\Lambda$CDM predictions, $q_0=-0.57\pm0.04$, $j_0=-1$ and $z_t=0.71\pm0.08$. We find that even very simple kinematic models are equally good to describe the data compared to the concordance $\Lambda$CDM model, and that the current observations are not powerful enough to discriminate among all of them.Comment: 13 pages. Matches published versio