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

Abstract

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.

Keywords

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

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

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

Publication Date

December 29, 2023

Submission Date

February 24, 2023

Acceptance Date

May 25, 2023

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Year 2023 Volume: 28 Number: 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. YYU JINAS. 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 (December 1, 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”, YYU JINAS, vol. 28, no. 3, pp. 919–934, Dec. 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 (December 1, 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. YYU JINAS. 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, vol. 28, no. 3, Dec. 2023, pp. 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. YYU JINAS. 2023 Dec. 1;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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