计算机科学
核糖开关
核糖核酸
核酸结构
折叠(DSP实现)
多样性(控制论)
计算生物学
人工智能
机器学习
计算模型
数据科学
非编码RNA
生物
工程类
电气工程
基因
生物化学
作者
Kevin Wu,James Zou,Howard Y. Chang
摘要
Abstract The three-dimensional structure of RNA molecules plays a critical role in a wide range of cellular processes encompassing functions from riboswitches to epigenetic regulation. These RNA structures are incredibly dynamic and can indeed be described aptly as an ensemble of structures that shifts in distribution depending on different cellular conditions. Thus, the computational prediction of RNA structure poses a unique challenge, even as computational protein folding has seen great advances. In this review, we focus on a variety of machine learning-based methods that have been developed to predict RNA molecules’ secondary structure, as well as more complex tertiary structures. We survey commonly used modeling strategies, and how many are inspired by or incorporate thermodynamic principles. We discuss the shortcomings that various design decisions entail and propose future directions that could build off these methods to yield more robust, accurate RNA structure predictions.
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