Computational prediction of RNA tertiary structures using machine learning methods

• URL: http://arxiv.org/abs/2009.01440v1
• Date: Thu, 3 Sep 2020 04:01:43 GMT
• Title: Computational prediction of RNA tertiary structures using machine learning methods
• Authors: Bin Huang, Yuanyang Du, Shuai Zhang, Wenfei Li, Jun Wang, Jian Zhang
• Abstract summary: Computational prediction approaches can help to understand RNA structures and their stabilizing factors. Although their usage in protein-related fields has a long history, the use of machine learning methods in predicting RNA tertiary structures is new and rare.
• Score: 14.35527588241679
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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