WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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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 Kolonoskopi Görüntülerinden Otomatik Ülseratif Kolit Teşhisi(Institute of Electrical and Electronics Engineers Inc., 2018-11) Kacmaz, Rukiye Nur; Yilmaz, BulentUlcerative colitis (UC) is a disease in which inner surface of colon is inflamed. Ulcers and open scars on the colon are observed. The complaint in the flare period is the frequent bloody diarrhea. Complaints of people with UC increase and decrease periodically. Colonoscopy is the most preferred approach for the visualization of the gastrointestinal tract for the diagnosis and follow-up of related diseases, and UC in particular. The lack of experience of the colonoscopist, complicated locality of the lesion, and the rush in the colonoscopy suite to complete the procedure as soon as possible may cause mistakes in visual analysis. In this study, 200 colonoscopy images (100 normal, 100 UC) were used. The statistical features such as gray level variance, gray level local variance, normalized variance, histogram range, and entropy were extracted from the images, and a normalized 200x5 feature matrix was formed. The normal images and images with UC were discriminated using support vector machines and k-nearest neighbors. It should be noted that the extraction of only 5 features from the colonoscopy images resulted in 95% accuracy. This study demonstrated the feasibility of the development of software tools for aiding the physicians in the diagnosis of colon diseases. © 2019 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 8Citation - Scopus: 9Meme Kanseri Histopatolojik Görüntülerinin Bilgisayar Destekli Sınıflandırılması(Institute of Electrical and Electronics Engineers Inc., 2017-10) Aksebzeci, Bekir Hakan; Kayaaltı, ÖmerNowadays, one of the most common types of cancer is breast cancer. The early and accurate diagnosis of breast cancer has great importance in the treatment of the disease. In the diagnosis of breast cancer, histopathological analysis of cell and tissue specimens taken by biopsy is considered as the gold standard. Histopathological analysis is a tedious process that is highly dependent on the knowledge and experience of the pathologists. In this study; it is aimed to develop a computer-Aided system that can reduce the workload of pathologists and help them in their diagnosis. An image set containing benign and malignant tumor images of breast cancer has been studied. To perform texture analysis on tumor images; first order statistics, Gabor and gray-level co-occurrence matrix (GLCM) feature extraction methods have been applied. Then, various classifiers were applied to the obtained feature matrices and their performances were compared. The highest classification accuracy was achieved 82.06% by Random Forests classifier with feature combination of Gabor and GLCM methods. The results presented here show that computer-Assisted diagnosis of breast cancer is a promising field. © 2018 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.