mRNA-CLA:用于预测 mRNA 亚细胞定位的可解释深度学习方法。

IF 4.2 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Yifan Chen , Zhenya Du , Xuanbai Ren , Chu Pan , Yangbin Zhu , Zhen Li , Tao Meng , Xiaojun Yao
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引用次数: 0

摘要

信使核糖核酸(mRNA)是转录后基因调控的关键,是蛋白质合成的直接模板。然而,现有预测 mRNA 亚细胞定位的方法需要改进和提高。值得注意的是,现有算法很少能注释具有多种定位的 mRNA 序列。在这项工作中,我们提出了 mRNA-CLA,这是一种创新的多标签 mRNA 亚细胞定位预测框架,它利用了一种具有多头自我关注机制的深度学习方法。该框架采用多尺度卷积层来提取不同区域的序列特征,并使用为每个序列明确设计的自我注意机制。与卷积神经网络层衍生的位置权重矩阵(PWM)搭配,我们的模型在分析中提供了可解释性。特别是,我们对来自不同亚细胞定位的 mRNA 序列进行了基底分析,以确定每个位点对应的核苷酸特异性。我们的评估结果表明,mRNA-CLA 模型大大优于现有的方法和工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
mRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization

Messenger RNA (mRNA) is vital for post-transcriptional gene regulation, acting as the direct template for protein synthesis. However, the methods available for predicting mRNA subcellular localization need to be improved and enhanced. Notably, few existing algorithms can annotate mRNA sequences with multiple localizations. In this work, we propose the mRNA-CLA, an innovative multi-label subcellular localization prediction framework for mRNA, leveraging a deep learning approach with a multi-head self-attention mechanism. The framework employs a multi-scale convolutional layer to extract sequence features across different regions and uses a self-attention mechanism explicitly designed for each sequence. Paired with Position Weight Matrices (PWMs) derived from the convolutional neural network layers, our model offers interpretability in the analysis. In particular, we perform a base-level analysis of mRNA sequences from diverse subcellular localizations to determine the nucleotide specificity corresponding to each site. Our evaluations demonstrate that the mRNA-CLA model substantially outperforms existing methods and tools.

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来源期刊
Methods
Methods 生物-生化研究方法
CiteScore
9.80
自引率
2.10%
发文量
222
审稿时长
11.3 weeks
期刊介绍: Methods focuses on rapidly developing techniques in the experimental biological and medical sciences. Each topical issue, organized by a guest editor who is an expert in the area covered, consists solely of invited quality articles by specialist authors, many of them reviews. Issues are devoted to specific technical approaches with emphasis on clear detailed descriptions of protocols that allow them to be reproduced easily. The background information provided enables researchers to understand the principles underlying the methods; other helpful sections include comparisons of alternative methods giving the advantages and disadvantages of particular methods, guidance on avoiding potential pitfalls, and suggestions for troubleshooting.
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