3D RNA Graph Representation Methods for Classification of RNA Molecules Using Graph Kernel and Graph Neural Network Methods

Enes Algül

doi:10.53433/yyufbed.1256154

TR EN

3D RNA Graph Representation Methods for Classification of RNA Molecules Using Graph Kernel and Graph Neural Network Methods

Öz

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.

Anahtar Kelimeler

3D RNA Graph Representations, Geometric Measurements, Graph Classifications, Graph Kernels, Graph Neural Networks

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yazarlar

Enes Algül ^*
0000-0001-6597-4242
Türkiye

Yayımlanma Tarihi

29 Aralık 2023

Gönderilme Tarihi

24 Şubat 2023

Kabul Tarihi

25 Mayıs 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 28 Sayı: 3

DOI

https://doi.org/10.53433/yyufbed.1256154

IZ

https://izlik.org/JA72JM48WA

APA

Algül, E. (2023). 3D RNA Graph Representation Methods for Classification of RNA Molecules Using Graph Kernel and Graph Neural Network Methods. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 28(3), 919-934. https://doi.org/10.53433/yyufbed.1256154

AMA

1.Algül E. 3D RNA Graph Representation Methods for Classification of RNA Molecules Using Graph Kernel and Graph Neural Network Methods. YYUFBED. 2023;28(3):919-934. doi:10.53433/yyufbed.1256154

Chicago

Algül, Enes. 2023. “3D RNA Graph Representation Methods for Classification of RNA Molecules Using Graph Kernel and Graph Neural Network Methods”. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi 28 (3): 919-34. https://doi.org/10.53433/yyufbed.1256154.

EndNote

Algül E (01 Aralık 2023) 3D RNA Graph Representation Methods for Classification of RNA Molecules Using Graph Kernel and Graph Neural Network Methods. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi 28 3 919–934.

IEEE

[1]E. Algül, “3D RNA Graph Representation Methods for Classification of RNA Molecules Using Graph Kernel and Graph Neural Network Methods”, YYUFBED, c. 28, sy 3, ss. 919–934, Ara. 2023, doi: 10.53433/yyufbed.1256154.

ISNAD

Algül, Enes. “3D RNA Graph Representation Methods for Classification of RNA Molecules Using Graph Kernel and Graph Neural Network Methods”. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi 28/3 (01 Aralık 2023): 919-934. https://doi.org/10.53433/yyufbed.1256154.

JAMA

1.Algül E. 3D RNA Graph Representation Methods for Classification of RNA Molecules Using Graph Kernel and Graph Neural Network Methods. YYUFBED. 2023;28:919–934.

MLA

Algül, Enes. “3D RNA Graph Representation Methods for Classification of RNA Molecules Using Graph Kernel and Graph Neural Network Methods”. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 28, sy 3, Aralık 2023, ss. 919-34, doi:10.53433/yyufbed.1256154.

Vancouver

1.Enes Algül. 3D RNA Graph Representation Methods for Classification of RNA Molecules Using Graph Kernel and Graph Neural Network Methods. YYUFBED. 01 Aralık 2023;28(3):919-34. doi:10.53433/yyufbed.1256154

3D RNA Graph Representation Methods for Classification of RNA Molecules Using Graph Kernel and Graph Neural Network Methods

Graf Çekirdek ve Graf Sinir Ağı Yöntemlerini Kullanarak RNA Moleküllerini Sınıflandırılmak İçin 3D RNA Graf Temsili Yöntemleri

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3D RNA Graph Representation Methods for Classification of RNA Molecules Using Graph Kernel and Graph Neural Network Methods

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