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

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Türk Doğa ve Fen Dergisi
Volume:12 Issue:2
Classifying RNA Strands with A Novel Graph Representation Based on the Sequence Free Energy

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

Pages : 32-39

View : 78 | Download : 26

Publication Date : 2023-06-22

Article Type : Research Paper

Abstract :ABSTRACT Ribonucleic acids insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(All Paths Cycle Embeddings insert ignore into journalissuearticles values(APC);, Shortest Paths Kernel/Embedding insert ignore into journalissuearticles values(SP);, and Weisfeiler - Lehman and Optimal Assignment Kernel insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(Needleman-Wunsch); used for DNA and RNA comparison.
Keywords : Graph representation, RNA, Graph Kernel, Machine Learning

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