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miCGR: interpretable deep neural network for predicting both site-level and gene-level functional targets of microRNA

文献类型：期刊论文

作者Wu, Xiaolong3,4; Zhang, Lehan2,3; Tong, Xiaochu2,3; Wang, Yitian2,3; Zhang, Zimei3; Kong, Xiangtai2,3; Ni, Shengkun2,3; Luo, Xiaomin2,3; Zheng, Mingyue2,3; Tang, Yun4
刊名BRIEFINGS IN BIOINFORMATICS
出版日期2024-11-26
卷号26期号:1页码:13
关键词MicroRNA target prediction chaos game representation deep learning convolutional neural network
ISSN号1467-5463
DOI10.1093/bib/bbae616
通讯作者Tang, Yun(ytang234@ecust.edu.cn) ; Li, Xutong(lixutong@simm.ac.cn)
英文摘要MicroRNAs (miRNAs) are critical regulators in various biological processes to cleave or repress translation of messenger RNAs (mRNAs). Accurately predicting miRNA targets is essential for developing miRNA-based therapies for diseases such as cancer and cardiovascular disease. Traditional miRNA target prediction methods often struggle due to incomplete knowledge of miRNA-target interactions and lack interpretability. To address these limitations, we propose miCGR, an end-to-end deep learning framework for predicting functional miRNA targets. MiCGR employs 2D convolutional neural networks alongside an enhanced Chaos Game Representation (CGR) of both miRNA sequences and their candidate target site (CTS) on mRNA. This advanced CGR transforms genetic sequences into informative 2D graphical representations based on sequence composition and subsequence frequencies, and explicitly incorporates important prior knowledge of seed regions and subsequence positions. Unlike one-dimensional methods based solely on sequence characters, this approach identifies functional motifs within sequences, even if they are distant in the original sequences. Our model outperforms existing methods in predicting functional targets at both the site and gene levels. To enhance interpretability, we incorporate Shapley value analysis for each subsequence within both miRNA sequences and their target sites, allowing miCGR to achieve improved accuracy, particularly with more lenient CTS selection criteria. Finally, two case studies demonstrate the practical applicability of miCGR, highlighting its potential to provide insights for optimizing artificial miRNA analogs that surpass endogenous counterparts.
WOS关键词CHAOS GAME REPRESENTATION ; RECOGNITION ; ALIGNMENT
资助项目National Key Research and Development Program of China[1965-2022]
WOS研究方向Biochemistry & Molecular Biology ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:001364209800001
出版者OXFORD UNIV PRESS
源URL[http://119.78.100.183/handle/2S10ELR8/314659]  
专题新药研究国家重点实验室
通讯作者Tang, Yun; Li, Xutong
作者单位1.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
2.Univ Chinese Acad Sci, Sch Phys Sci, YuQuan Rd 19A, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China
4.East China Univ Sci & Technol, Sch Pharm, 130 Meilong Rd, Shanghai 200237, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Xiaolong,Zhang, Lehan,Tong, Xiaochu,et al. miCGR: interpretable deep neural network for predicting both site-level and gene-level functional targets of microRNA[J]. BRIEFINGS IN BIOINFORMATICS,2024,26(1):13.
APA Wu, Xiaolong.,Zhang, Lehan.,Tong, Xiaochu.,Wang, Yitian.,Zhang, Zimei.,...&Li, Xutong.(2024).miCGR: interpretable deep neural network for predicting both site-level and gene-level functional targets of microRNA.BRIEFINGS IN BIOINFORMATICS,26(1),13.
MLA Wu, Xiaolong,et al."miCGR: interpretable deep neural network for predicting both site-level and gene-level functional targets of microRNA".BRIEFINGS IN BIOINFORMATICS 26.1(2024):13.

入库方式： OAI收割

来源：上海药物研究所

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