Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 1The Identification of Discriminative Single Nucleotide Polymorphism Sets for the Classification of Behçet's Disease(Institute of Electrical and Electronics Engineers Inc., 2018-09) Görmez, Yasin; Işik, Yunus Emre; Bakir-Güngör, BurcuBehçet's disease is a long-term multisystem inflammatory disorder, characterized by recurrent attacks affecting several organs. As the genotyping individuals get cheaper and easier following the developments in genomic technologies, genome-wide association studies (GWAS) emerged. By this means, via studying big-sized case-control groups for a specific disease, potential genetic variations, single nucleotide polymorphisms (SNPs) are identified. Although several genetic risk factors are identified for Behçet's disease with the help of these studies via scanning around a million of SNPs, these variations could only explain up to 20% of the disease's genetic risk. In this study, for Behçet's disease classification, via comparing all the SNPs genotyped in GWAS, with the SNPs selected via using genetic knowledge, gain ratio and information gain; both reduction in the feature size and improvement in the classification accuracy is aimed. Also, using different classification algorithms such as random forest, k-nearest neighbour and logistic regression, their effects on the classification accuracy are investigated. Our results showed that compared to other feature selection methods, with at least 81% success rate, the selection of the SNPs using the genetic information (of their GWAS p-values, indicating the significance of the SNP against the disease) provides 15% to 42% improvement in all classification algorithms. This improvement is statistically sound. While gain ratio and information gain feature selection techniques yield similar classification accuracies, the models using all SNPs could not exceed 50% accuracies and results in the worst performance. © 2019 Elsevier B.V., All rights reserved.Conference Object Identify Commonly Affected Pathways in Psychiatric Diseases(Institute of Electrical and Electronics Engineers Inc., 2018-09) Bulut, Umit; Bakir-Güngör, BurcuGenome-wide association studies (GWAS) are an extraordinary source of information when it comes to revealing the common variations of human complex diseases. Until now, the large amount of data generated from these studies have not been shown its full potential enough to identify the molecular and functional framework to be able to understand how a molecular system works. Following a more specific perspective, this study focused on the identification of commonly affected pathways of psychiatric diseases. The pathway term as used in molecular biology, depicts a simplified model of a process within the cell or tissue. Lately, several GWAS datasets are publicly available for various disease types such as psychiatric, immune-related, neurodegenerative, cardiovascular and such. A study on each disease and pairwise comparison to understand the behavior of disease and system would be time consuming and exhaustive. Instead of handling the results of these studies one by one, grouping diseases by target points is a more efficient way. This work aims to get one step closer to reveal key points of diseases and target these points to develop personalized medicine approaches. Especially for complex diseases, every drug doesn't show the same effect in every people. This paper contains the definition of molecular pathways, methods to identify disease related pathways, and to find common pathways pairwise in psychiatric diseases. © 2019 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 22Citation - Scopus: 52Evaluation of Classification Algorithms, Linear Discriminant Analysis and a New Hybrid Feature Selection Methodology for the Diagnosis of Coronary Artery Disease(Institute of Electrical and Electronics Engineers Inc., 2018-12) Kolukisa, Burak; Hacilar, Hilal; Göy, Gökhan; Kus, Mustafa; Bakir-Güngör, Burcu; Aral, Atilla; Güngör, Vehbi ÇağrıAccording to the World Health Organization (WHO), 31% of the world's total deaths in 2016 (17.9 million) was due to cardiovascular diseases (CVD). With the development of information technologies, it has become possible to predict whether people have heart diseases or not by checking certain physical and biochemical values at a lower cost. In this study, we have evalated a set of different classification algorithms, linear discriminant analysis and proposed a new hybrid feature selection methodology for the diagnosis of coronary heart diseases (CHD). Throughout this research effort, using three publicly available Heart Disease diagnosis datasets (UCI Machine Learning Repository), we have conducted comparative performance evaluations in terms of accuracy, sensitivity, specificity, F-measure, AUC and running time. © 2023 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Koroner Arter Hastalığı Tanısı İçin Alan Bilgisi İçeren Topluluk Öznitelik Seçim Yöntemi(Institute of Electrical and Electronics Engineers Inc., 2020-10-05) Kolukisa, Burak; Güngör, Vehbi Çağrı; Bakir-Güngör, Burcu; Gungor, Burcu BakirCoronary Artery Disease (CAD) is the condition where, the heart is not fed enough as a result of the accumulation of fatty matter called atheroma in the walls of the arteries. In 2016, CAD accounts for 31% (17.9 million) of the world's total deaths and its diagnosis is difficult. It is estimated that approximately 23.6 million people will die from this disease in 2030. With the development of machine learning and data mining techniques, it might be possible to diagnose CAD inexpensively and easily via examining some physical and biochemical values. In this study, for the CAD classification problem, a novel ensemble feature selection methodology that incorporates domain knowledge is proposed. Via applying the proposed methodology on the UCI Cleveland CAD dataset and using different classification algorithms, performance metrics are compared. It is shown that in our experiments, when Multilayer Perceptron classifier is used with 9 selected features, our proposed solution reached 85.47% accuracy, 82.96% accuracy and 0.839 F-Measure. As a future work, we aim to generate a machine learning model that can quickly diagnose CAD on real-time data in hospitals. © 2021 Elsevier B.V., All rights reserved.