Gaussian graphical approaches in estimation of biological systems

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Fen Edebiyat Fakültesi, İstatistik Bölümü, Türkiye

Tezin Onay Tarihi: 2013

Öğrenci: EZGİ AYYILDIZ

Danışman: VİLDA PURUTÇUOĞLU GAZİ

Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu

Özet:

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.