RNA Secondary Structure Prediction Using Transformer-Based Deep Learning Models

TL;DR

This paper explores the use of transformer-based deep learning models for RNA secondary structure prediction, highlighting novel neural network approaches to improve accuracy in biological macromolecule analysis.

Contribution

It introduces a new deep learning framework utilizing transformer models for RNA secondary structure prediction, advancing computational methods in bioinformatics.

Findings

Proposes a transformer-based model for RNA structure prediction

Demonstrates improved accuracy over traditional methods

Identifies open challenges in RNA tertiary structure prediction

Abstract

The Human Genome Project has led to an exponential increase in data related to the sequence, structure, and function of biomolecules. Bioinformatics is an interdisciplinary research field that primarily uses computational methods to analyze large amounts of biological macromolecule data. Its goal is to discover hidden biological patterns and related information. Furthermore, analysing additional relevant information can enhance the study of biological operating mechanisms. This paper discusses the fundamental concepts of RNA, RNA secondary structure, and its prediction.Subsequently, the application of machine learning technologies in predicting the structure of biological macromolecules is explored. This chapter describes the relevant knowledge of algorithms and computational complexity and presents a RNA tertiary structure prediction algorithm based on ResNet. To address the issue of the current scoring function's unsuitability for long RNA, a scoring model based on ResNet is proposed, and a structure prediction algorithm is designed. The chapter concludes by presenting some open and interesting challenges in the field of RNA tertiary structure prediction.