Classifying RNA Strands with A Novel Graph Representation Based on the Sequence Free Energy

Enes Algül

Makale detayları

Classifying RNA Strands with A Novel Graph Representation Based on the Sequence Free Energy

Yazarlar:

Enes Algül

Yayıncı:

Bingol University

Ana Başlıklar:

Mühendislik

Dergi:

Türk Doğa ve Fen Dergisi Vol.12 Issue 2, pp.32-39

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001292413

pISSN:

2149-6366

Anahtar kelimeler:

Özet:

 ABSTRACT Ribonucleic acids RNA are macromolecules in all living cell, and they are mediators between DNA and protein. Structurally, RNAs are more similar to the DNA. In this paper, we introduce a compact graph representation utilizing the Minimum Free Energy MFE of RNA molecules secondary structure. This representation represents structural components of secondary RNAs as edges of the graphs, and MFE of these components represents their edge weights. The labeling process is used to determine these weights by considering both the MFE of the 2D RNA structures, and the specific settings in the RNA structures. This encoding is used to make the representation more compact by giving a unique graph representation for the secondary structural elements in the graph. Armed with the representation, we apply graph-based algorithms to categorize RNA molecules. We also present the result of the cutting-edge graph-based methods All Paths Cycle Embeddings APC, Shortest Paths KernelEmbedding SP, and Weisfeiler - Lehman and Optimal Assignment Kernel WLOA on our dataset 1 using this new graph representation. Finally, we compare the results of the graph-based algorithms to a standard bioinformatics algorithm Needleman-Wunsch used for DNA and RNA comparison.

 ABSTRACT Ribonucleic acids RNA are macromolecules in all living cell, and they are mediators between DNA and protein. Structurally, RNAs are more similar to the DNA. In this paper, we introduce a compact graph representation utilizing the Minimum Free Energy MFE of RNA molecules secondary structure. This representation represents structural components of secondary RNAs as edges of the graphs, and MFE of these components represents their edge weights. The labeling process is used to determine these weights by considering both the MFE of the 2D RNA structures, and the specific settings in the RNA structures. This encoding is used to make the representation more compact by giving a unique graph representation for the secondary structural elements in the graph. Armed with the representation, we apply graph-based algorithms to categorize RNA molecules. We also present the result of the cutting-edge graph-based methods All Paths Cycle Embeddings APC, Shortest Paths KernelEmbedding SP, and Weisfeiler - Lehman and Optimal Assignment Kernel WLOA on our dataset 1 using this new graph representation. Finally, we compare the results of the graph-based algorithms to a standard bioinformatics algorithm Needleman-Wunsch used for DNA and RNA comparison.

 ABSTRACT Ribonucleic acids RNA are macromolecules in all living cell, and they are mediators between DNA and protein. Structurally, RNAs are more similar to the DNA. In this paper, we introduce a compact graph representation utilizing the Minimum Free Energy MFE of RNA molecules secondary structure. This representation represents structural components of secondary RNAs as edges of the graphs, and MFE of these components represents their edge weights. The labeling process is used to determine these weights by considering both the MFE of the 2D RNA structures, and the specific settings in the RNA structures. This encoding is used to make the representation more compact by giving a unique graph representation for the secondary structural elements in the graph. Armed with the representation, we apply graph-based algorithms to categorize RNA molecules. We also present the result of the cutting-edge graph-based methods All Paths Cycle Embeddings APC, Shortest Paths KernelEmbedding SP, and Weisfeiler - Lehman and Optimal Assignment Kernel WLOA on our dataset 1 using this new graph representation. Finally, we compare the results of the graph-based algorithms to a standard bioinformatics algorithm Needleman-Wunsch used for DNA and RNA comparison.

 ABSTRACT Ribonucleic acids RNA are macromolecules in all living cell, and they are mediators between DNA and protein. Structurally, RNAs are more similar to the DNA. In this paper, we introduce a compact graph representation utilizing the Minimum Free Energy MFE of RNA molecules secondary structure. This representation represents structural components of secondary RNAs as edges of the graphs, and MFE of these components represents their edge weights. The labeling process is used to determine these weights by considering both the MFE of the 2D RNA structures, and the specific settings in the RNA structures. This encoding is used to make the representation more compact by giving a unique graph representation for the secondary structural elements in the graph. Armed with the representation, we apply graph-based algorithms to categorize RNA molecules. We also present the result of the cutting-edge graph-based methods All Paths Cycle Embeddings APC, Shortest Paths KernelEmbedding SP, and Weisfeiler - Lehman and Optimal Assignment Kernel WLOA on our dataset 1 using this new graph representation. Finally, we compare the results of the graph-based algorithms to a standard bioinformatics algorithm Needleman-Wunsch used for DNA and RNA comparison.

Makale detayları

Classifying RNA Strands with A Novel Graph Representation Based on the Sequence Free Energy

Yazarlar:

Enes Algül

Yayıncı:

Bingol University

Ana Başlıklar:

Mühendislik

Dergi:

Türk Doğa ve Fen Dergisi Vol.12 Issue 2, pp.32-39

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001292413

pISSN:

2149-6366

Graph representation

RNA

Graph Kernel

Machine LearningGraph representation

RNA

Graph Kernel

Machine Learning

Sayı

Makale detayları

Yazarlar: Enes Algül

Yayıncı: Bingol University

Ana Başlıklar: Mühendislik

Dergi: Türk Doğa ve Fen Dergisi Vol.12 Issue 2, pp.32-39

Belge Tipi: Makale

Yayın Tarihi: 6/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001292413

pISSN: 2149-6366

Sayı

Yazarlar:

Enes Algül

Yayıncı:

Bingol University

Ana Başlıklar:

Mühendislik

Dergi:

Türk Doğa ve Fen Dergisi Vol.12 Issue 2, pp.32-39

Belge Tipi:

Makale

Yayın Tarihi:

6/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001292413

pISSN:

2149-6366