Abstract：
The accurate \"base pairing\" in RNA molecules, which leads to the prediction of RNA secondary structures, is crucial in order to explain unknown biological operations. Recently, COVID-19, a widespread disease, has caused many deaths, affecting humanity in an unprecedented way. SARS-CoV-2, a single-stranded RNA virus, has shown the significance of analyzing these molecules and their structures. This paper aims to create a pioneering framework in the direction of predicting specific RNA structures, leveraging syntactic pattern recognition. The proposed framework, Knotify+, addresses the problem of predicting H-type pseudoknots, including bulges and internal loops, by featuring the power of context-free grammar (CFG). We combine the grammar\'s advantages with maximum base pairing and minimum free energy to tackle this ambiguous task in a performant way. Specifically, our proposed methodology, Knotify+, outperforms state-of-the-art frameworks with regards to its accuracy in core stems prediction. Additionally, it performs more accurately in small sequences and presents a comparable accuracy rate in larger ones, while it requires a smaller execution time compared to well-known platforms. The Knotify+ source code and implementation details are available as a public repository on GitHub.
摘要：
RNA分子中准确的“碱基配对”，这导致了RNA二级结构的预测，对于解释未知的生物操作至关重要。最近,COVID-19，一种广泛传播的疾病，造成了很多人死亡,以前所未有的方式影响人类。SARS-CoV-2是一种单链RNA病毒,显示了分析这些分子及其结构的重要性。本文旨在在预测特定RNA结构的方向上创建一个开创性的框架，利用句法模式识别。拟议的框架，Know+,解决了预测H型伪结的问题，包括凸起和内部回路，通过展示上下文无关语法(CFG)的力量。我们将语法的优势与最大碱基配对和最小自由能相结合，以高性能的方式解决这个模糊的任务。具体来说,我们提出的方法，Know+,在核心茎预测的准确性方面优于最先进的框架。此外,它在小序列中表现得更准确，在大序列中表现出相当的准确率，而与知名平台相比，它需要的执行时间更短。Knoto+源代码和实现详细信息作为GitHub上的公共存储库提供。
