Reparametrizing the Sigmoid Model of Gene Regulation for Bayesian Inference

摘要

This poster describes a novel work-in-progress reparametri-zation of a frequently used non-linear ordinary differential equation (ODE) model for inferring gene regulations from expression data. We show that in its commonly used form, the model cannot always determine the sign of the regulatory effect as well as other parameters of the model. The proposed reparametrization makes inference over the model stable and amenable to fully Bayesian treatment with state of the art Hamiltonian Monte Carlo methods.