Factored expectation propagation for input-output FHMM models in systems biology
This work addresses a specific modeling challenge in systems biology, but it appears incremental as it builds on existing variational methods with a novel approximation technique.
The authors tackled the problem of jointly modeling metabolic signals and gene expression in systems biology by proposing an input-output factorial hidden Markov model with a structured variational inference approach, using expectation propagation to approximate expectations, and validated it through simulations and a real-world bacterial dataset.
We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications. We propose an approach based on input-output factorial hidden Markov models and propose a structured variational inference approach to infer the structure and states of the model. We start from the classical free form structured variational mean field approach and use a expectation propagation to approximate the expectations needed in the variational loop. We show that this corresponds to a factored expectation constrained approximate inference. We validate our model through extensive simulations and demonstrate its applicability on a real world bacterial data set.