Bayesian inference of biological networks whose components are linearly dependent

2017-06-01
International Conference on Progress in Applied Science, (01 Haziran 2017)

Suggestions

Bayesian inference of the complex MAPK pathway under structural dependency
Purutçuoğlu Gazi, Vilda (2008-06-01)
Bayesian inference for the MAPK/ERK pathway by considering the dependency of the kinetic parameters
Purutçuoğlu Gazi, Vilda (null; 2007-06-01)
Bayesian inference for the MAPK/ERK pathway by considering the dependency of the kinetic parameters
Purutçuoğlu Gazi, Vilda (Institute of Mathematical Statistics, 2008-01-01)
The MAPK/ERK pathway is one of the major signal transduction systems which regulates the cellular growth control of all eukaryotes like the cell proliferation and the apoptosis. Because of its importance in cellular lifecycle, it has been studied intensively, resulting in a number of qualitative descriptions of this regulatory mechanism. In this study we describe the MAPK/ERK pathway as an explicit set of reactions by combining different sources. Our reaction set takes into account the localization and diff...
Bayesian inference of deterministic MAPK-ERK pathway via reversible jumps Monte Carlo method
Purutçuoğlu Gazi, Vilda (null; 2016-06-01)
Bayesian modelling for asymmetric multi-modal circular data
Kılıç, Muhammet Burak; Kalaylıoğlu Akyıldız, Zeynep Işıl; Sengupta, Ashis; Department of Statistics (2015)
In this thesis, we propose a Bayesian methodology based on sampling importance re-sampling for asymmetric and bimodal circular data analysis. We adopt Dirichlet process (DP) mixture model approach to analyse multi-modal circular data where the number of components is not known. For the analysis of temporal circular data, such as hourly measured wind directions, we join DP mixture model approach with circular times series modelling. The approaches are illustrated with both simulated and real life data sets. ...
Citation Formats
V. Purutçuoğlu Gazi, “Bayesian inference of biological networks whose components are linearly dependent,” presented at the International Conference on Progress in Applied Science, (01 Haziran 2017), İstanbul, Türkiye, 2017, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/85175.