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DeepHFFT-m7G:用于RNA m7G修饰识别的双通道自我注意和混合功能融合框架

Yongxian Fan¹, Zeheng Wu¹, Guicong Sun¹

¹Guilin University of Electronic Technology, School of Computer Science and Information Security, Guilin, 541004, Guangxi, China.

Computational biology and chemistry

|November 28, 2025

概括

我们开发了DeepHFFT-m7G,这是一种新的深度学习方法,可以准确地识别RNA中的N7-甲基瓜诺辛 (m7G) 修饰位. 这一进步通过提高关键RNA功能元素的预测来改善疾病研究.

科学领域:

*RNA生物学和表观遗传学
* 计算生物学和生物信息学
* 基因组学中的深度学习应用

背景情况:

*N7-甲基瓜诺辛 (m7G) 是一种关键的RNA修饰,与人类疾病有关.
* 现有的深度学习方法用于m7G站点预测的准确性低于最佳.
*准确识别m7G位点对于了解RNA功能和疾病机制至关重要.

研究的目的:

* 提出一种新的深度学习方法,DeepHFFT-m7G,以有效和准确地预测RNA序列中的m7G修饰位.
* 整合混合功能融合和双通道自我注意网络,以提高预测性能.
* 超越现有的最先进的方法来识别m7G网站.

主要方法:

*混合特征融合:集成经典RNA序列特征与RNA2Vec嵌入.
*双通道CNN用于本地特征提取和变压器编码用于全球特征提取.
*多层感知子 (MLP) 用于最终的m7G位点预测.

主要成果:

* DeepHFFT-m7G实现了高性能指标:AUROC (97.53%),准确性 (96.92%),MCC (93.93%) 和特异性 (97.63%).
* 拟议的方法显著优于现有的最先进的方法 (SOTA).
*比较实验和可视化分析证实了DeepHFFT-m7G.的优越性和概括能力.

结论:

* DeepHFFT-m7G代表了预测RNA中的m7G修饰位的重大进展.
*该方法的高精度和稳定性为RNA功能研究和疾病研究提供了宝贵的工具.
* 这项工作突出了在计算表观遗传学中混合特征融合和注意力机制的潜力.

关键词:

深度学习是一种深度学习.N7 - 甲基瓜诺辛 (m7G) 是一种预测RNA序列的时间变压器变压器变压器

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