Gaussian graphical approaches in estimation of biological systems

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2013

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Ayyıldız, Ezgi

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The Gaussian Graphical Model (GGM) is one of the well-known deterministic inference methods which is based on the conditional independency of nodes in the system. In this study we consider to implement this approach in small and relatively large networks under different singularity and sparsity conditions. In inference of these systems we perform lasso and L-1 penalized lasso regression approaches and select the best ﬁtted model to the data by using different criteria. Among many alternatives, we apply the F-measure, false positive rate, precision, and recall measures as well as cross validation method in Monte Carlo runs. According to the results of their accuracies and computational time, we choose the best criterion for the inference of realistically complex systems such as the JAK-STAT pathway. In the calculation in case we can face with singularity problem, we evaluate the performance of a recently developed technique for the matrix decompositions. This novel approach also enables us to deal with the computational problems caused by the sparsity of the networks. Finally, apart from the current model selection approaches in the GGM ﬁeld, we investigate other plausible alternatives for this type of inference problems.

Subject Keywords

Biological systems., Gaussian processes., Biomathematics., Biometry.

URI

http://etd.lib.metu.edu.tr/upload/12615946/index.pdf
https://hdl.handle.net/11511/22631

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

E. Ayyıldız, “Gaussian graphical approaches in estimation of biological systems,” M.S. - Master of Science, Middle East Technical University, 2013.