Bayesian Inference and Mathematical Modeling in Complex Biological Systems
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".
Deadline for manuscript submissions: 27 May 2024 | Viewed by 466
Special Issue Editor
Special Issue Information
Dear Colleagues,
Bayesian inference provides a principled foundation for the modeling of biological systems accounting for uncertainty. In particular, Bayesian modeling procedures can provide means of imposing prior knowledge and by explicitly accounting for noise and parameter uncertainty provide added robustness when compared to conventional maximum likelihood-based estimation procedures. Furthermore, Bayesian inference provides principled tools for model assessments guiding model selection and checking.
The aim of this Special Issue is to highlight the use of Bayesian inference for the modeling of complex biological systems. Data pertaining to complex biological systems are typically noisy and the parameters of the mathematical models used to characterize these systems are subject to uncertainty. This Special Issue will highlight how mathematical modeling procedures endowed uncertainty quantification through Bayesian inference procedures can provide important tools in order to characterize the structure and further our understanding of complex biological systems in the face of uncertainty.
Mathematical models of complex biological systems include matrix and tensor factorization-based techniques, statistical network modeling approaches, simulation models, and deep learning methodologies for the characterization of complex biological systems. Furthermore, complex biological datasets may only be partially observed, heterogeneous and include multiple sources of information to be combined. This Special Issue will highlight how Bayesian inference procedures is useful including in these disparate contexts of mathematical modeling of complex biological systems.
The topics of this Special Issue include but are not limited to:
- Bayesian inference in matrix and tensor-based decomposition methods for biological data;
- Bayesian inference for the modeling of complex biological networks;
- Bayesian inference for data fusion of complex biological data;
- Bayesian deep learning for the modeling of biological data;
- Bayesian model assessment in biological data modeling;
- Bayesian optimization in complex biological systems.
Prof. Dr. Morten Mørup
Guest Editor
Manuscript Submission Information
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