A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder.

Abstract

RNAs play essential roles in diverse physiological and pathological processes by interacting with other molecules (RNA/protein/compound), and various computational methods are available for identifying these interactions. However, the encoding features provided by existing methods are limited and the existing tools does not offer an effective way to integrate the interacting partners. In this study, a task-specific encoding algorithm for RNAs and RNA-associated interactions was therefore developed. This new algorithm was unique in (a) realizing comprehensive RNA feature encoding by introducing a great many of novel features and (b) enabling task-specific integration of interacting partners using convolutional autoencoder-directed feature embedding. Compared with existing methods/tools, this novel algorithm demonstrated superior performances in diverse benchmark testing studies. This algorithm together with its source code could be readily accessed by all user at: https://idrblab.org/corain/ and https://github.com/idrblab/corain/.

• Authors: Yunxia Wang, Ziqi Pan, Minjie Mou, Weiqi Xia, Hongning Zhang, Hanyu Zhang, Jin Liu, Lingyan Zheng, Yongchao Luo, Hanqi Zheng, Xinyuan Yu, Xichen Lian, Zhenyu Zeng, Zhaorong Li, Bing Zhang, Mingyue Zheng, Honglin Li, Tingjun Hou, Feng Zhu
• Journal: Nucleic acids research (Nucleic Acids Res) Vol. 51 Issue 21 Pg. e110 (Nov 27 2023) ISSN: 1362-4962 [Electronic] England
• PMID: 37889083 (Publication Type: Journal Article)
• Copyright: © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.

Chemical References

• RNA

Topics

• RNA (genetics)
• Computational Biology (methods)
• Algorithms
• Software