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评估和深度神经网络的开发,以使用autoBioSeqpypy进行RNA5-Methyluridine分类

Lezheng Yu¹, Yonglin Zhang², Li Xue³

¹School of Chemistry and Materials Science, Guizhou Education University, Guiyang, China.

Frontiers in microbiology

|June 5, 2023

概括

这项研究优化了深度学习模型,用于预测5-甲基尤里丁 (m5U) RNA的修饰. 开发的Deepm5U工具准确地识别了m5U网站,为表表转录组调节提供了洞察力,并指导了未来的RNA修饰算法开发.

科学领域:

分子生物学分子生物学
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

RNA的修改,集体称为表表写体,对于基因表达调节至关重要.
深度学习 (DL) 显示了预测RNA修改位点的潜力,但对于5-甲基尤里丁 (m5U) 等特定修改的最佳架构仍然不清楚.

研究的目的:

评估和识别最佳的DL架构用于m5U网站预测.
开发一个准确的DL模型,Deepm5U,用于m5U站点识别.
提供指导,开发和分析DL模型在表皮转录学.

主要方法:

使用autoBioSeqpy进行m5U站点分类的DL模型的比较评估.
优化DL架构参数 (层深,神经元宽度).
卷积循环神经网络 (Deepm5U) 的开发和应用.
使用LayerUMAP和DeepSHAP进行模型解释.

主要成果:

确定了用于m5U预测的最佳DL架构和参数.
在各种数据集和技术中,Deepm5U在预测m5U站点方面表现出高准确度.
模型解释技术为RNA修饰的DL模型行为提供了见解.

结论:

Deepm5U 是一个有效的工具,用于转录omewide m5U 概况.
该研究为RNA修饰研究中的DL模型开发提供了实际指导方针.
了解DL模型机制可以提高表体转录学预测的可靠性.

关键词:

在RNA5甲基尤里丁中.这就是 SHAP SHAP 的意思.这就是UMAP UMAP.在 autoBioSeqpy 中使用.深度学习是一种深度学习.

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