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 limit...

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Bibliographic Details
Published in:Nucleic acids research Vol. 51; no. 21; p. e110
Main Authors: Wang, Yunxia, Pan, Ziqi, Mou, Minjie, Xia, Weiqi, Zhang, Hongning, Zhang, Hanyu, Liu, Jin, Zheng, Lingyan, Luo, Yongchao, Zheng, Hanqi, Yu, Xinyuan, Lian, Xichen, Zeng, Zhenyu, Li, Zhaorong, Zhang, Bing, Zheng, Mingyue, Li, Honglin, Hou, Tingjun, Zhu, Feng
Format: Journal Article
Language:English
Published: England Oxford University Press 27.11.2023
Subjects:
Algorithms
Computational Biology - methods
Methods
RNA - genetics
Software
ISSN:0305-1048, 1362-4962, 1362-4962
Online Access:Get full text
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Summary: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/. Graphical Abstract Graphical Abstract
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The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
ISSN:0305-1048
1362-4962
1362-4962
DOI:10.1093/nar/gkad929