We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A Bayesian approach to reconstructing genetic regulatory networks with hidden factors.
- Authors
Beal, Matthew J; Falciani, Francesco; Ghahramani, Zoubin; Rangel, Claudia; Wild, David L
- Abstract
We have used state-space models (SSMs) to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T cell activation. SSMs are a class of dynamic Bayesian networks in which the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be directly measured in a gene expression profiling experiment, for example: genes that have not been included in the microarray, levels of regulatory proteins, the effects of mRNA and protein degradation, etc.
- Publication
Bioinformatics (Oxford, England), 2005, Vol 21, Issue 3, p349
- ISSN
1367-4803
- Publication type
Journal Article
- DOI
10.1093/bioinformatics/bti014