论文标题
使用机器学习方法对RNA三级结构的计算预测
Computational prediction of RNA tertiary structures using machine learning methods
论文作者
论文摘要
RNA在生物过程中起着至关重要的多功能作用。计算预测方法可以帮助理解RNA结构及其稳定因素，从而提供有关其功能的信息，并促进新RNA的设计。在过去的几年中，机器学习（ML）技术在许多领域取得了巨大进步。尽管它们在蛋白质相关领域中的使用历史悠久，但在预测RNA三级结构中使用ML方法是新的且罕见的。在这里，我们回顾了在RNA结构预测上使用ML方法的最新进展，并讨论在现场应用时的优点和限制，这些方法的困难和潜力。
RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.
