Browsing by Author "Göy, Gökhan"

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Citation - WoS: 22
Citation - Scopus: 51
Evaluation 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) Kolukisa, Burak; Hacilar, Hilal; Göy, Gökhan; Kus, Mustafa; Bakir-Güngör, Burcu; Aral, Atilla; Güngör, Vehbi Çağrı; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
According 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.
Citation - WoS: 1
Citation - Scopus: 1
Kolon Kanserinde Etkilenen Yolak Alt Ağlarini Ve Kümelenmelerini Belirlemek için Yeni Bir Yöntem
(Institute of Electrical and Electronics Engineers Inc., 2019) Göy, Gökhan; Ünlü Yazici, Miray; Bakir-Güngör, Burcu; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi; 04. Yaşam ve Doğa Bilimleri Fakültesi; 04.01. Biyomühendislik
Nowadays 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.
Citation - Scopus: 2
Makine Öǧrenmesi Teknikleri Ile İnternet Servis Saǧlayıcısı için Müşteri Kayıp Tahmini
(Institute of Electrical and Electronics Engineers Inc., 2020) Göy, Gökhan; Kolukisa, Burak; Bahçevan, Cenk Anıl; Güngör, Vehbi Çağrı; 01. Abdullah Gül University
With the developing technology in every fields, a competitive marketing environment has been arised. In this competitive environment, analyzing customer behavior has become vital. In particular, the ability to easily change any service provider has become very critical for the company to continue its existence. At the same time, the amount of financial resources spent on retaining customers much less than to obtain new clients. In this context, the traditional methods of examining vast amount of data obtained today for establishing decision support systems have lost their validities. In this study, we used a dataset which is provided by TurkNet serving as an internet service provider in Turkey. Various preprocessing steps has performed on this dataset and then classification algorithms ran. Afterwards results have obtained and compared. The results of these experiments analyzed in terms of the area under the curve value. In this context, the most successful classifier algorithm has been determined as the Random Trees algorithm with a value of 0.936. © 2020 Elsevier B.V., All rights reserved.
Citation - Scopus: 5
Makine Öğrenmesi Yöntemleri ile Kredi Kartı Sahteciliğinin Tespiti
(Institute of Electrical and Electronics Engineers Inc., 2019) Göy, Gökhan; Gezer, Cengiz; Güngör, Vehbi Çağrı; 01. Abdullah Gül University
With the increase in credit card usage of people, the credit card transactions increase dramatically. It is difficult to identify fraudulent transactions among the vast amount of credit card transactions. Although credit card fraud is limited in number of transactions, it causes serious problems in terms of financial losses for individuals and organizations. Even though large number of studies has been conducted to solve this problem, there is no generally accepted solution. In this paper, a publicly available data set is used. The unbalance problem of the data set was solved by using hybrid sampling methods together. On this data set, comparative performance evaluations have been conducted. Different from other studies, the Area Under the Curve (AUC) metric, which expresses the success in such data sets, has also been used in addition to standard performance metrics. Since it is also important to quickly detect credit card fraud transactions; the running time of different methods is also presented as another performance metric. © 2020 Elsevier B.V., All rights reserved.
Özellik Gruplaması ve Sıralaması ile Birlikte miRNA ve mRNA Ekspresyon Profillerinin Makine Öğrenimi Tabanlı Entegrasyonu
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2021) Göy, Gökhan; Güngör, Burcu; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
Hastalıkların oluşum ve gelişim mekanizmalarını moleküler seviyede anlamak çok önemlidir. Hastalığa yol açan fonksiyonel mekanizmaların açığa vurulması, yalnızca hastalıkların moleküler tanısına değil, aynı zamanda yeni tedavi yöntemlerinin geliştirilmesine de katkıda bulunur. Bugünlerde, teknolojideki ilerlemeler sayesinde moleküler veriler eski zamanların aksine daha ucuz fiyatlarla elde edilebilir. Bu erişime açık verilerin entegre edilmesi, özellikle kanser gibi kompleks oluşum ve ilerleme mekanizması olan hastalıkların moleküler mekanizmalarını anlamak için elzemdir. Bu tezde, kanser hastalarını doğru sınıflandırmak için, mRNA ve mikroRNA verilerini (moleküler seviyede iki tip –omik veri) entegre eden miRcorrNet ve miRMUTINet adında iki adet araç geliştirildi. 11 kanser tipi için, örneklerin mRNA ve miRNA ekspresyon profilleri, The Cancer Genome Atlas'tan indirildi. İki veri tipi, hem Pearson Korelasyon Katsayısı, hem de Ortak Bilgi metrikleri kullanılarak entegre edildi. 100 katlı Monte Karlo Çapraz Doğrulama kullandığımız deneylerimizde, her iki araç için de 99% Area Under the Curve skoru elde ettik. Geliştirilen yöntemler bağımsız veri kümeleri ile de test edildi. Biyolojik doğrulama amacıyla, her kanser tipi için, önemli olduğu belirlenen miRNAlar ve genler listesi üzerinde, fonksiyonel zenginleştirilme analizi gerçekleştirildi. Ayrıca, her kanser tipi için, hastalıklar ile ilgili olduğu düşünülen mRNA ve miRNA'ler literatür validasyonuna tabi tutulmuş ve bulguların dikkate değer olduğu görülmüştür.
Citation - Scopus: 25
Recursive Cluster Elimination Based Rank Function (SVM-RCE-R) Implemented in KNIME
(F1000 Research Ltd, 2021) Yousef, Malik; Bakir-Güngör, Burcu; Jabeer, Amhar; Göy, Gökhan; Qureshi, Rehman A.; C Showe, Louise; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
In our earlier study, we proposed a novel feature selection approach, Recursive Cluster Elimination with Support Vector Machines (SVM-RCE) and implemented this approach in Matlab. Interest in this approach has grown over time and several researchers have incorporated SVM-RCE into their studies, resulting in a substantial number of scientific publications. This increased interest encouraged us to reconsider how feature selection, particularly in biological datasets, can benefit from considering the relationships of those genes in the selection process, this led to our development of SVM-RCE-R. SVM-RCE-R, further enhances the capabilities of SVM-RCE by the addition of a novel user specified ranking function. This ranking function enables the user to stipulate the weights of the accuracy, sensitivity, specificity, f-measure, area under the curve and the precision in the ranking function This flexibility allows the user to select for greater sensitivity or greater specificity as needed for a specific project. The usefulness of SVM-RCE-R is further supported by development of the maTE tool which uses a similar approach to identify MicroRNA (miRNA) targets. We have also now implemented the SVM-RCE-R algorithm in Knime in order to make it easier to applyThe use of SVM-RCE-R in Knime is simple and intuitive and allows researchers to immediately begin their analysis without having to consult an information technology specialist. The input for the Knime implemented tool is an EXCEL file (or text or CSV) with a simple structure and the output is also an EXCEL file. The Knime version also incorporates new features not available in SVM-RCE. The results show that the inclusion of the ranking function has a significant impact on the performance of SVM-RCE-R. Some of the clusters that achieve high scores for a specified ranking can also have high scores in other metrics. © 2021 Elsevier B.V., All rights reserved.