Gene networks inference using dynamic Bayesian networks

By B. Perrin, Liva Ralaivola, Aurélien Mazurie, S. Bottani, J. Mallet, Florence d'Alché-Buc. European Conference on Computational Biology, 2003

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Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, background): “A Bayesian network is a graph-based model of joint multivariate probability distributions that captures properties of conditional independence between variables [45].”
Inferring cellular networks – a review (2007, background): “They are often augmented with hidden nodes [ 57 ], which can describe transcription factor activity [47] or any other kind of environmental or non-transcriptional effects in the cell [58-61].”
Gene regulatory network inference: Data integration in dynamic models - A review (2009, methodology): “as presented Polisetty et al. (2006) or Generalized Mass Action models and by Vilela et al.”
A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data (2005, background): “Dynamic Bayesian network (DBN) analysis (Murphy and Mian, 1999; Imoto et al., 2002; Kim et al., 2003; Perrin et al., 2003) is well-suited for handling time-series gene expression data.”