Time series

Bayesian forecasting of temporal gene expression by using an autoregressive panel data approach

M. Nascimento, Silva, F. Fe, Sáfadi, T., Nascimento, A. C. C., Barroso, L. M. A., Glória, L. S., B. Carvalho, deS., Nascimento, M., Silva, F. Fe, Sáfadi, T., Nascimento, A. C. C., Barroso, L. M. A., Glória, L. S., and B. Carvalho, deS., 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.

Analysis of key genes and modules during the courses of traumatic brain injury with microarray technology

X. - Y. Zhang, Gu, C. - G., Gu, J. - W., Zhang, J. - H., Zhu, H., Zhang, Y. - C., Cheng, J. - M., Li, Y. - M., and Yang, T., Analysis of key genes and modules during the courses of traumatic brain injury with microarray technology, vol. 13, pp. 9220-9228, 2014.

Gene expression data acquired at different times after traumatic brain injury (TBI) were analyzed to identify differentially expressed genes (DEGs). Interaction network analysis and functional enrichment analysis were performed to extract valuable information, which may benefit diagnosis and treatment of TBI. Microarray data were downloaded from Gene Expression Omnibus and pre-treated with MATLAB. DEGs were screened out with the SAM method.

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