Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Impact of Gene Duplicate Handling Strategies on Classification Performance and Feature Selection in Gene Expression Data(Institute of Electrical and Electronics Engineers Inc., 2025-09-17) Kuzudisli, Cihan; Qaqish, Bahjat; Gungor, Burcu Bakir; Yousef, MalikConference Object Comparison of Disease Specific Sub-Network Identification Programs(Institute of Electrical and Electronics Engineers Inc., 2018-09) Dedeturk, Beyhan Adanur; Bakir-Güngör, Burcu; Adanur, Beyhun; Gungor, Burcu BakirActive sub-network search aims to identify a group of interconnected genes in a protein-protein interaction network that contains most of the disease-associated genes. In recent years, to address active sub-network search problem, various algorithms and programs are developed. In this study, performances of disease specific sub-network identification programs are compared. The same input dataset is run in jActiveModules, ActiveSubnetworkGA, CytoHubba, ClusterViz, MCODE, CytoMOBAS, PathFindR, PINBPA and PEWCC programs. Then, functional enrichment analysis is applied on obtained sub-networks. Finally, they are compared according to the results of GO Enrichment Analysis. In addition to these, work performances, features and requirements of programs are compared. © 2019 Elsevier B.V., All rights reserved.Conference Object Hepatoselüler Karsinom Oluşumunda Etkili Moleküler Mekanizmaların İn Siliko Yöntemlerle Araştırılması(Institute of Electrical and Electronics Engineers Inc., 2020-09) Doǧan, Refika Sultan; Saka, Samed; Bakir-Güngör, Burcu; Gungor, Burcu BakirHepatocellular carcinoma (HCC) is the most common cause of cancer-related death in the world. The molecular changes in the organism during the development of HCC are not fully understood. The aim of the present study is to contribute to the identification of critical genes and pathways associated with HCC via integrating various bioinformatics methods. In this study, experiments were conducted on gene expression data of 14 HCC tissues and noncancerous control tissues. A total of 1229 genes, which show a statistically significant change between the groups, were identified. Among these, 681 genes were upregulated and 548 genes were downregulated. Via mapping the detected genes into protein protein interaction networks, active subnetwork search, subnetwork topological analysis and functional enrichment analyses were carried out. The interactions between the molecular pathways affected by HCC were also presented. © 2020 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.