亚细胞定位
信使核糖核酸
计算生物学
深度学习
人工智能
生物
细胞生物学
计算机科学
基因
遗传学
作者
Yifan Chen,Zheyuan Du,Xin Ren,Pao‐Shin Chu,Yuan Zhu,Zhen Li,Tao Meng,Xiaojun Yao
出处
期刊:Methods
[Elsevier]
日期:2024-05-01
标识
DOI:10.1016/j.ymeth.2024.04.018
摘要
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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