Reparametrizing the Sigmoid Model of Gene Regulation for Bayesian Inference.

0496098 - MBÚ 2019 RIV CH eng C - Conference Paper (international conference)
Modrák, Martin
Reparametrizing the Sigmoid Model of Gene Regulation for Bayesian Inference.
Berlín: Springer, 2018. Subseries of Lecture Notes in Computer Science. ISBN 978-3-319-99428-4. In: Computational Methods in Systems Biology. Chan: Springer, 2018 - (Češka, M.; Šafránek, D.), s. 309-312. ISBN 978-3-319-99428-4.
[16th International Conference, CMSB 2018. Brno (CZ), 12.09.2018-14.09.2018]
R&D Projects: GA MŠMT(CZ) LM2015055
Institutional support: RVO:61388971
Keywords : Sigmoid Model * Hamiltonian Monte Carlo methods
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

This poster describes a novel work-in-progress reparametrization 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 Sigmoid Model with state of the art Hamiltonian Monte Carlo methods. Complete source code and a more detailed explanation of the model is available at https://github.com/cas-bioinf/genexpi-stan.

Permanent Link: http://hdl.handle.net/11104/0288911