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.

讨论了 RNA 和 RNA 的二级结构及其预测的基本概念，以及机器学习技术在预测生物大分子结构中的应用，并提出了基于 ResNet 的 RNA 三级结构预测算法和打分模型。

使用基于 Transformer 的深度学习模型来进行 RNA 二级结构预测

RNA Secondary Structure Prediction Using Transformer-Based Deep Learning  Models

RNA secondary structure is modeled with the novel arbitrary-order hidden
Markov model ({\alpha}-HMM). The {\alpha}-HMM extends over the traditional HMM
with capability to model stochastic events that may be in influenced by
historically distant ones, making it suitable to account for long-range
canonical base pairings between nucleotides, which constitute the RNA secondary
structure. Unlike previous heavy-weight extensions over HMM, the {\alpha}-HMM
has the flexibility to apply restrictions on how one event may influence
another in stochastic processes, enabling efficient prediction of RNA secondary
structure including pseudoknots.

用新的任意阶隐马尔可夫模型（α-HMM）对 RNA 二级结构进行建模，通过模拟受历史上较远事件影响的随机事件，适合描述核苷酸之间的长程典型碱基配对，构成 RNA 二级结构，并且与之前的复杂扩展相比，α-HMM 具有对随机过程中一个事件如何影响另一个事件的限制，从而能够高效地预测包括伪结在内的 RNA 二级结构。