Direk, Tugay

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https://hdl.handle.net/20.500.14365/7743
Name Variants
Direk, Tugay
Job Title
Email Address
tugay.direk@ieu.edu.tr
Main Affiliation
05.04. Software Engineering
Status
Current Staff
Website
ORCID ID
0000-0003-0325-0403
Scopus Author ID
58880204100
Turkish CoHE Profile ID
B619F6A93F29234E
Google Scholar ID
hP4ExYQAAAAJ
WoS Researcher ID
KFQ-1931-2024
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vesika_1000 - Copy.jpg (1.3 MB)

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4

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2

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2

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5

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1

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1

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0.50

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1.25

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Now showing 1 - 4 of 4
  • Thumbnail Image
    Article
    Citation - WoS: 2
    Citation - Scopus: 5
    Image Denoising by Linear Regression on Non-Local Means Algorithm
    (Springer Science and Business Media Deutschland GmbH, 2024) Direk, Tugay
    Non-local means (NL-Means) algorithm which removes the noise from the image have been used in the field widely due to its good performance especially for magnetic resonance images which consists of three dimensional data. Its main idea is using all the pixels which are local and non-local in an image and taking weighted averaging of all values. One negative side of this method is that it considers all pixels in the image without looking at their similarity. This paper proposes an NL-Means algorithm with pixel selection by applying linear regression analysis using root mean squared error (RMSE) value. After regression analysis, RMSE of the neighborhoods is used to exclude non-similar pixels during the noise removal. Lastly, obtained results were compared by four different methods which are NL-Means algorithm and, Gaussian, anisotropic diffusion and median filterings. All of the methods were outperformed by our method on structured similarity index and peak signal-to-noise ratio quantitative metrics. Moreover, the level of increase on visual qualities are also represented as a qualitative analysis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
  • Thumbnail Image
    Master Thesis
    Computational Identification of G Quadruplexes Secondary Structures
    (İzmir Ekonomi Üniversitesi, 2022) Direk, Tugay; Doluca, Osman; Oğuz, Kaya
    G-dörtlü yapılarının ikincil yapı sınıflandırması için bir standart eksikliği uzun bir süredir bulunmaktadır. Çıkıntı yapan G-bölgelerinin ve uyumsuz G-dörtlülerinin keşfiyle durum, hayal edilenden daha da karmaşık bir hal almıştır. Bu nedenle, günümüze kadar G4 ikincil yapılarını tanımlamak için bir standart getirmeyi amaçlayan sınırlı sayıda çalışma yapılmıştır. Bu çalışmada, üç boyutlu yapısal verileri kullanarak G-dörtlülerinin ikincil yapılarının tanımlanması için yeni bir yöntem öneriyoruz. Kısaca, tetradları ve döngüleri tanımlamak için guaninlerin koordinatları işlenir. Daha sonra, her bir tetrada katılan guaninleri ve döngüleri gösteren ikincil yapıyı bir figür şeklinde sunuyoruz. Ayrıca, dörtlü ve dörtlü yapıların topoloji tabanlı sınıflandırmasına dayalı ONZ sınıflandırması uygulanmış ve çalışmamızın sonuçları bununla karşılaştırılmıştır.
  • Thumbnail Image
    Article
    Computational Identification and Illustrative Standard for Representation of Unimolecular G-Quadruplex Secondary Structures (ciis-Gq)
    (Springer, 2024) Direk, Tugay; Doluca, Osman
    G-quadruplexes refer to a large group of nucleic acid-based structures. In recent years, they have been attracting attention due to their biological roles in the telomeres and promoter regions. These structures show wide diversity in topology, however, development of methods for structural classification of G-quadruplexes has been evaded for a long time. There has been a limited number of studies aiming to bring forth a secondary structure classification method. The situation was even more complex than imagined, since the discovery of bulged and mismatched G-quadruplexes while most of the available tools fail to distinguish these non-canonical G-quadruplex motifs. Moreover, the interpretation of their analysis output still requires expert knowledge. In this study, we propose a new method for identification of unimolecular G-Quadruplexes and classification by secondary structures based on three-dimensional structural data. Briefly, coordinates of guanines are processed to identify tetrads, loops and bulges. Then, we present the secondary structure in the form of a depiction which shows the loop types, bulges, and guanines that participate in each tetrad. Moreover, CIIS-GQ identifies non-guanine nucleotides that joins the G-tetrads and forms multiplets. Finally, the results of our study are compared with DSSR and ElTetrado classification methods, and the advantages of the proposed depiction method for representing secondary structures were discussed. The source code of the method can be accessed via https://github.com/TugayDirek/CIIS-GQ.
  • Thumbnail Image
    Conference Object
    Optimized Radix Sort for Efficient Health Data Sorting in Biomedical Backend Systems
    (Institute of Electrical and Electronics Engineers Inc., 2025) Direk, Tugay