Deterministic modeling and inference of biological systems

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Date

2017

Author

Seçilmiş, Deniz

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The mathematical description of biological networks can be performed mainly by stochastic and deterministic models. The former gives more information about the system, whereas, it needs very detailed measurements. On the other hand, the latter is relatively less informative, but, the collection of their data is easier than the stochastic ones, rendering it a more preferable modeling approach. In this study, we implement the deterministic modeling of biological systems due to the underlying advantage. Among many alternatives, we use the Gaussian graphical model (GGM) and evaluate its performance with respect to the random forest algorithm, which we suggest as an alternative approach of GGM. We estimate the model parameters, i.e., the structure of the networks, and then assess their findings based on their accuracies. Finally, we extend the study by using copulas in the description of data and apply the same modeling approaches to assess their effects.

Subject Keywords

Biometry., Biomathematics., Random data (Statistics)., Copulas (Mathematical statistics).

URI

http://etd.lib.metu.edu.tr/upload/12620914/index.pdf
https://hdl.handle.net/11511/26403

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Graduate School of Informatics, Thesis

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Citation Formats

D. Seçilmiş, “Deterministic modeling and inference of biological systems,” M.S. - Master of Science, Middle East Technical University, 2017.