Please use this identifier to cite or link to this item:
Type: Artigo
Title: Bayesian forecasting of temporal gene expression by using an autoregressive panel data approach
Author: Nascimento, M.
Silva, F.F.
Sáfadi, T.
Nascimento, A.C.C.
Barroso, L.M.A.
Glória, L.S.
Carvalho, B.S.
Abstract: 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
Subject: Inferência bayesiana
Country: Brasil
Rights: Aberto
Identifier DOI: 10.4238/gmr.15027299
Date Issue: 2016
Appears in Collections:IMECC - Artigos e Outros Documentos

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.