Application of deep intensive learning in RNA secondary structure prediction

Abstract

With the development of the novel coronavirus epidemic, virus detection and research has gradually become a hot research direction. The structure of the virus is mainly divided into protein shell and ribonucleic acid (RNA). RNA is an important information-carrying biopolymer within biological cells that plays a key role in regulatory processes and transcription control. Studies of RNA-induced conditions, including human immunodeficiency viruses, neocymavirus, and even Alzheimer's and Parkinson's disease, require an understanding of the structure and function of RNA. As a result, the study of RNA is becoming increasingly important in a range of applications, including biology and medicine. The function of RNA is determined primarily by the thermodynamic three-stage folding of a sequence of nucleotides. The hydrogen bond between nucleotides determines the main driving force for the formation of a three-stage structure. Smaller folds around the hydrogen bond are called secondary structures of RNA. The three-stage structure determines the function and nature of RNA, and traditional manual exploration of RNA tertiary structures, such as X-ray crystal diffraction, and MRI to determine RNA tertiary structures, while accurate and reliable, is labor-intensive and time-consuming. Accurate judgment of secondary structures has greatly influenced the study of RNA tertiary structures and deeper studies, and the exploration of RNA secondary structures with artificial intelligence can lead to more accurate, rapid and efficient results. In the current field, artificial intelligence algorithms to predict RNA secondary structures usually use deep learning, genetic algorithms and other means, through neural network fitting to obtain prediction results. This approach is supervised learning, requiring a large amount of RNA secondary structure data to be collated prior to the study, while the models trained are not explanatory. As we all know, RNA folding is driven primarily by thermodynamics, can we train a model that learns the principles of RNA folding on its own, based on limited structural data? The main research direction of this paper is to explore the secondary structure model of ribonucleic acid independently by using algorithms in the way of computer deep-strengthening learning. Deep-enhanced learning primarily transforms the prediction process of the RNA secondary structure into the process of intelligent decision-making to explore optimal decision-making. Due to the limited training set and computing power, this paper explores the feasibility and development potential of deep-enhanced learning algorithms in RNA secondary structure prediction. In the current field, artificial intelligence algorithms to predict RNA secondary structures usually use deep learning, genetic algorithms and other means, through neural network fitting to obtain prediction results. This approach is supervised learning, requiring a large amount of RNA secondary structure data to be collated prior to the study, while the models trained are not explanatory. As we all know, RNA folding is driven primarily by thermodynamics, can we train a model that learns the principles of RNA folding on its own, based on limited structural data? The main research direction of this paper is to explore the secondary structure model of ribonucleic acid independently by using algorithms in the way of computer deep-strengthening learning. Deep-enhanced learning primarily transforms the prediction process of the RNA secondary structure into the process of intelligent decision-making to explore optimal decision-making. Due to the limited training set and computing power, this paper explores the feasibility and development potential of deep-enhanced learning algorithms in RNA secondary structure prediction. The main research direction of this paper is to explore the secondary structure model of ribonucleic acid independently by using algorithms in the way of computer deep-strengthening learning. Deep-enhanced learning primarily transforms the prediction process of the RNA secondary structure into the process of intelligent decision-making to explore optimal decision-making. Due to the limited training set and computing power, this paper explores the feasibility and development potential of deep-enhanced learning algorithms in RNA secondary structure prediction.