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The agents interact using an underlying interaction network structure and get into consensus in their opinions, while finding better solutions to the problem.
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These conditional independencies can then be used to constrain the underlying network structure.
We use a Bayesian network, structured according to the underlying ontology, to model the prior probability of a protein's function.
In the current work, we reanalyze the CS-HM transcriptional data in the light of an underlying molecular interaction network.
Then, using the constructed interaction network, we studied the proteins that directly interacted with the virus and co-expressed genes.
The resulting parameter set can be used to identify the underlying regulatory network structure as well as to make forward-looking estimates of its function under temporal dynamics or environmental changes.
Jiang et al. also tried to use score-based Bayesian network structure learning methods to detect epistatic interactions [ 52].
CMGRN http://bioinfo.icts.hkbu.edu.hk/cmgrn/) is based on a methodology combining Bayesian hierarchical model with Gibbs Sampling implementation to identify correct sets of regulator-gene interactions which explore underlying network structures [ 47].
The static version of this model on a fixed (directed) underlying network structure has some limitations.
The formation and evolution of groups of synchronized oscillators reveal a non-trivial underlying network structure.
These proteins interact to form an intricate protein interaction network at a large scale.
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