Factored expectation propagation for input-output FHMM models in systems biology

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 eld 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.

• Publication year

  2013

• Venue

  arXiv: Machine Learning

• Publication date

  2013-05-17

• Fields of study

  Biology, Mathematics, Computer Science

• Identifiers
  arXiv 1305.4153
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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