Figure 1.

The NETGEM model. This figure presents a high-level description of the NETGEM model and the resulting inference algorithm. Here Λ, Θ are hyper-parameters and β is the strain damping factor. The parameters to be learnt are the functional category component for each edge (αe) and the functional category transition probability matrix (Qh). The inference is done over the hidden variables , where and are the random variables corresponding to the active functional category and the interaction strength for edge e at time t respectively. The inferred interaction dynamics is used to compute the edge and functional category change scores sT (e) and respectively.

Jethava et al. BMC Bioinformatics 2011 12:327   doi:10.1186/1471-2105-12-327
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