Model comparison for gynecological cancer datasets and selection of threshold value

Bahçivancı, Başak
Cancer is a very common system’s disease with its structural and functional complexities caused by high dimension and serious correlation of genes as well as sparsity of gene interactions. Hereby, different mathematical models have been suggested in the literature to unravel these challenges. Among many alternates, in this study we use the Gaussian graphical model, Gaussian copula graphical model and loop-based multivariate adaptive regression splines with/without interaction models due to their advantages over others from simulated datasets. In the first part of the thesis, we apply these models in our quasi-true cancer network by implementing real microarray datasets. The gynecological cancer is the second leading cancer type in women after the breast cancer. But there are less studies about it regarding the breast cancer because of its sociological reasons. Herein, initially, we detect the related literature and generate a list of core genes for this illness. Then, we construct a quasi-true network from these genes. Finally, we infer this network via underlying models and assess their accuracies. Hence, we can realistically evaluate the performance of these models in an actual disease’s system. In these analyses, we also observe that the estimates of models highly depend on their threshold values which convert estimated strengths of gene interactions as binary form to construct the graphical network. Thereby, in the second part of the thesis, we propose a novel approach for the selection of this value by considering the topology of networks and assess our performance via accuracy and computational time.


Graphical models in inference of biological networks
Farnoudkia, Hajar; Purutçuoğlu Gazi, Vilda; Department of Statistics (2020)
In recent years, particularly, on the studies about the complex system’s diseases, better understanding the biological systems and observing how the system’s behaviors, which are affected by the treatment or similar conditions, accelerate with the help of the explanation of these systems via the mathematical modeling. Gaussian Graphical Models (GGM) is a model that describes the relationship between the system’s elements via the regression and represents the states of the system via the multivariate Gaussia...
Cancer modeling via biologically validated genes
Karakelle, Barış Su; Purutçuoğlu Gazi, Vilda; Ürün, Yüksel (2017-05-10)
The cancer disease is the second most common disease type seen after the frequency of the cardiovascular diseases. The frequency of this genetic disease changes with respect to the gender. Accordingly, the gynecological cancer, which covers ovarian, endometrial or cervical cancer, is the second most common cancer type in women after the breast cancer. Similar to other cancer types, the gynecological cancer is the system disease, meaning that the malfunctions and mutations in the gene regulatory ...
Analyses and modeling of ovarian cancer microarray data
Karakelle, Barış S; Purutçuoğlu Gazi, Vilda; Department of Biomedical Engineering (2019)
Ovarian cancer is one of the common cancer types among other oncological diseases. The major causes of this cancer can be listed as age, obesity, hormone therapy, material inheritance and contraceptive pills. Due to its generality and importance, many researches have been conducted from distinct labs about this illness and its plausible causes have been intensively investigated either inmicroarray studies, where just part of the related genes are detected, or in thepairwise correlation analyses between the ...
Novel BRCA2 pathogenic genotype and breast cancer phenotype discordance in monozygotic triplets
Duzkale, Neslihan; EYERCİ, NİLNUR; Oksuzoglu, Berna; Teker, Taner; Kandemir, Olcay (Elsevier BV, 2020-04-01)
BRCA1/2 genes with high-penetrance are tumor suppressor and tumor susceptibility genes that play important roles in the homologous recombination mechanism in DNA repair and increase breast cancer risk. Variants in BRCA1 or BRCA2 are the main causes of familial and early-onset breast cancer. This study investigated pathogenic variant belonging to the BRCA2 gene splice region in monozygotic triplets. A 44-year-old woman was diagnosed with breast cancer when she was 32 years old. Her monozygotic sister had a h...
Inference of the stochastic MAPK pathway by modified diffusion bridge method
Purutçuoğlu Gazi, Vilda (2013-03-01)
The MAPK pathway is one of the well-known systems in oncogene researches of eukaryotes due to its important role in cell life. In this study, we perform the parameter estimation of a realistic MAPK system by using western blotting data. In inference, we use the modified diffusion bridge algorithm with data augmentation technique by modelling the realistically complex system via the Euler-Maruyama approximation. This approximation, which is the discretized version of the diffusion model, can be seen as an al...
Citation Formats
B. Bahçivancı, “Model comparison for gynecological cancer datasets and selection of threshold value,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Statistics., Middle East Technical University, 2019.