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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Patton, K.L. John, D.J. Norris, J.L. Lewis, D.R. Muday, G.K. |
| Copyright Year | 2004 |
| Abstract | Microarray technology allows for the collection of multiple replicates of gene expression time course data for hundreds of genes at a handful of time points. Developing hypotheses about a gene transcriptional network, based on time course gene expression data is an important and very challenging problem. In many situations there are similarities which suggest a hierarchical structure between the replicates. This paper develops posterior probabilities for network features based on multiple hierarchical replications. Through Bayesian inference, in conjunction with the Metropolis-Hastings algorithm and model averaging, a hierarchical multiple replicate algorithm is applied to seven sets of simulated data and to a set of Arabidopsis thaliana gene expression data. The models of the simulated data suggest high posterior probabilities for pairs of genes which have at least moderate signal partial correlation. For the Arabidopsis model, many of the highest posterior probability edges agree with the literature. |
| Sponsorship | IEEE Computer Society |
| Starting Page | 336 |
| Ending Page | 346 |
| Page Count | 11 |
| File Size | 1203957 |
| File Format | |
| ISSN | 15455963 |
| Volume Number | 11 |
| Issue Number | 2 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Mathematical model Computational modeling Correlation Gene expression Data models Bayes methods Educational institutions model averaging Bayesian modeling hierarchical posterior probability multiple replicates gene expression modeling |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Genetics Biotechnology |
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