WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
Browse
Search Results
Conference Object Investigation of Hepatocellular Carcinoma Molecular Mechanisms via in Silico Analyses(IEEE, 2020-09) Dogan, Refika Sultan; Saka, Samed; 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 non-cancerous 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.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 İmmün Bağlantılı Hastalıklarda Aktif Alt Ağ Araması ile Ortak Hastalık Oluşum Mekanizmalarının Tespiti(IEEE, 2020-09) Eryilmaz, Mahmut Kaan; Kuzudisli, Cihan; Gungor, Burcu BakirDifferent, but related diseases often contain shared symptoms indicating the presence of possible overlaps in underlying pathogenic mechanisms. The identification of the shared pathways and related factors across these diseases helps to better understand the causes of these diseases, to prevent and treat these diseases. In this study, using immune-related diseases, we proposed a new method on how to compare the development mechanisms of related diseases based on biological pathways. Following the developments in genomic technologies, the genotyping gets cheaper and easier, and hence 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) could he identified. With the help of these studies, in which around a million of SNPs are scanned, the variations and genes that could have a role in specific disease development could be detected. In this study, via using available GWAS datasets and human protein-protein interaction network, and via detecting active subnetworks and affected pathways, seven immune related diseases are analyzed. Via investigating the similarities among the identified pathways for related diseases, we aim to define the underlying pathogenic mechanisms, and hence to contribute to the elucidation of disease development mechanisms and to the drug repositioning studies.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.