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Reparametrizing the Sigmoid Model of Gene Regulation for Bayesian Inference.
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SYSNO ASEP 0496098 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Reparametrizing the Sigmoid Model of Gene Regulation for Bayesian Inference. Author(s) Modrák, Martin (MBU-M) ORCID Issue data Berlín: Springer, 2018 ISBN 978-3-319-99428-4 Source Title Computational Methods in Systems Biology. - Chan : Springer, 2018 / Češka Martin ; Šafránek David - ISBN 978-3-319-99428-4 Pages s. 309-312 Series Subseries of Lecture Notes in Computer Science Number of pages 4 s. Publication form Print - P Action 16th International Conference, CMSB 2018 Event date 12.09.2018 - 14.09.2018 VEvent location Brno Country CZ - Czech Republic Event type WRD Language eng - English Country CH - Switzerland Keywords Sigmoid Model ; Hamiltonian Monte Carlo methods Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects LM2015055 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Institutional support MBU-M - RVO:61388971 UT WOS 000453218400020 EID SCOPUS 85053213495 DOI 10.1007/978-3-319-99429-1_20 Annotation 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.
Workplace Institute of Microbiology Contact Eliška Spurná, eliska.spurna@biomed.cas.cz, Tel.: 241 062 231 Year of Publishing 2019
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