WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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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 Nöromüsküler Hastalıkların Ortak MikroRNA ve Yolaklarının İn Siliko Yöntemlerle Belirlenmesi(Institute of Electrical and Electronics Engineers Inc., 2019-04) Ünlü Yazici, Miray; Aksu-Menges, Evrim; Akkaya-Ulum, Yeliz Z.; Balcihayta, Burcu; Bakir-Güngör, BurcuNeuromuscular disorders (NMD) are a heterogeneous group of diseases characterized by the loss of function of the peripheral nerves and muscles. However, there are no effective and widespread therapeutic approaches to prevent or delay the progression of these disease types. MicroRNAs (miRNAs) which cause significant changes in gene expression by binding to target messenger RNAs (mRNAs), are known to have an effect on disease mechanisms. In this study, by integrating different bioinformatics methods, we aim to find miRNAs, target genes and pathways related to a group of neuromuscular diseases. For this purpose, we determined 17 miRNAs that show significant expression changes between patient and healthy groups; predicted target genes of these miRNAs; and identified affected pathways using subnetwork discovery, functional enrichment based algorithms. In our study, we integrated different in-silico approaches that proceed in topdown manner or bottom-up manner. The identified candidate miRNAs, genes and pathways, which could help to explain neuromuscular disease development mechanisms, are now under investigation in wet-lab. © 2020 Elsevier B.V., All rights reserved.Conference Object Papiller Tiroid Karsinom Oluşumunda Etkili Moleküler Mekanizmaların İn Siliko Yöntemlerle Tespit Edilmesi(Institute of Electrical and Electronics Engineers Inc., 2019-04) Ersöz, Nur Sebnem; Guzel, Yasin; Bakir-Güngör, BurcuRepresenting approximately 70% to 80% of thyroid cancers, papillary thyroid cancer (PTC) is the most common type of thyroid cancers. PTC is seen in all age groups, but it is seen more frequently in women than in men. Detection of biomarker proteins of papillary thyroid cancinoma plays an important role in the diagnosis of the disease. In this study, we aim to find target genes and pathways that are associated with papillar thyroid carcinoma, by integrating different bioinformatics methods. For this purpose, usingin-silico methodologies, candidate genes and pathways that could explain disease development mechanisms are identified. Throughout this study, firstly we identified differentially expressed genes as the amount of their protein product differ between patient and healthy groups. Secondly, by using active subnetworks search algorithms, topologic analyses and functional enrichment tests, candidate proteins,which could be thought as PTC biomarkers, and affected pathways are identified. © 2020 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 4Citation - Scopus: 10Sağlıkta Blokzincir Tabanlı Sistem Bilişimi Uygulamaları(Institute of Electrical and Electronics Engineers Inc., 2020-10-05) Dedeturk, Beyhan Adanur; Bakir-Güngör, Burcu; Soran, Ahmet; Adanur, BeyhanRecently, the use of blockchain technology in the field of healthcare has increased. Although blockchain technology brought several innovations to healthcare, still there are problems waiting to be resolved. In order to provide alternative solutions to these problems, the use of fog computing together with blockchain technology has been proposed. In this study, the applications of blockchain based fog computing technology in healthcare are investigated. The aim of this study is to provide the readers an idea about the interactive use of blockchain and fog computing in the field of healthcare. For this purpose, firstly, fog computing and blockchain technologies are introduced. Afterwards, the integration of these areas, the advantages and disadvantages of using these technologies in the field of healthcare is discussed and a new system architecture is proposed. © 2021 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.Conference Object Citation - WoS: 1Citation - Scopus: 1Kolon Kanserinde Etkilenen Yolak Alt Ağlarini Ve Kümelenmelerini Belirlemek için Yeni Bir Yöntem(Institute of Electrical and Electronics Engineers Inc., 2019-09) Göy, Gökhan; Ünlü Yazici, Miray; Bakir-Güngör, BurcuNowadays new technological developments that play an important role in the production of big data have brought about the interpretation, sharing and storage of data related to complex diseases. Combining multi-omic data in different molecular levels is potentially important for understanding the biological origin of complex diseases. One of these complex diseases is cancer of different types, which has one of the highest causes of death worldwide. The integration of multiple omic data in the framework of a comprehensive analysis and identification of relevant pathways contribute to the development of therapeutic approaches related to disease. In this study, RNA and methylation data (genes and p values) of colon adenocarcinoma were obtained from TCGA data portal and combined with Fisher's method. While protein subnetworks affected by the disease were identified by using subnetwork algorithm, pathways related to the disease and genes associated with these pathways were determined by functional enrichment analysis. Using gene-pathway relationship matrix, kappa scores of pathways were determined by similarity calculation. In this way, the pathways were clustered according to the hierarchically optimal number, as a result, the most important pathway clusters and related genes that are effective in disease formation identified. © 2020 Elsevier B.V., All rights reserved.