TY  - JOUR
T1  - 3D RNA Graph Representation Methods for Classification of RNA Molecules Using Graph Kernel and Graph Neural Network Methods
TT  - Graf Çekirdek ve Graf Sinir Ağı Yöntemlerini Kullanarak RNA Moleküllerini Sınıflandırılmak İçin 3D RNA Graf Temsili Yöntemleri
AU  - Algül, Enes
PY  - 2023
DA  - December
DO  - 10.53433/yyufbed.1256154
JF  - Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi
JO  - YYUFBED
PB  - Van Yuzuncu Yıl University
WT  - DergiPark
SN  - 1300-5413
SP  - 919
EP  - 934
VL  - 28
IS  - 3
LA  - en
AB  - Ribonucleic acids (RNAs) are nucleic acid types with 1D/2D/3D structural shapes and are essential for sustaining life. These structural shapes of the RNAs are highly correlated with their functions. While the primary and secondary structures of RNA have been extensively studied, the tertiary structure has received relatively less attention. In this article, we present novel approaches for representing 3D RNA structures as graph data, employing geometric measurements such as Base position, Square root velocity function (SRVF), Arc length, and Curvature. Then, we utilise kernel methods and neural network methods to predict RNA functions. Our findings demonstrate the effectiveness of these methodologies in unraveling the functional attributes of RNA molecules, thus enriching our understanding of their complex biological significance.
KW  - 3D RNA Graph Representations
KW  - Geometric Measurements
KW  - Graph Classifications
KW  - Graph Kernels
KW  - Graph Neural Networks
N2  - Ribonükleik asitler (RNA'lar), 1B/2B/3B yapısal şekillere sahip nükleik asit türleri olup, yaşamı sürdürmek için hayati öneme sahiptirler. RNA'ların bu yapısal şekilleri, fonksiyonlarıyla yüksek derecede ilişkilidir. RNA'nın birincil ve ikincil yapıları kapsamlı bir şekilde incelenirken, üçüncül yapı nispeten daha az dikkat çekmiştir. Bu makalede, Baz konumu, Karekök hız fonksiyonu (SRVF), Yay uzunluğu ve Eğrilik gibi geometrik ölçümler kullanarak 3B RNA yapılarını grafik verileri olarak temsil etmeye yönelik yeni yaklaşımlar sunuyoruz. Daha sonra, çekirdek (kernel) yöntemleri ve sinir ağı (neural network) yöntemleri kullanarak RNA fonksiyonlarını tahmin ediyoruz. Bulgularımız, bu metodolojilerin RNA moleküllerinin fonksiyonel özelliklerini çözmedeki etkinliğini gösteriyor ve böylece onların karmaşık biyolojik önemine dair anlayışımızı zenginleştiriyor.
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UR  - https://doi.org/10.53433/yyufbed.1256154
L1  - https://dergipark.org.tr/en/download/article-file/2973357
ER  -