一个可解释的混合CNN-LSTM框架,用于精确的基于RNAN6-甲基氨酸 (m6A) 修饰的序列分类
Kainat Ali Rehman1, Muhammad Sohail Khan1, Faiza Tila2
1Department of Computer Software Engineering, University of Engineering and Technology Mardan, Mardan 23200, Pakistan.
SLAS technology
|December 7, 2025
概括
这项研究引入了CNN-LSTM模型与SHAP用于准确的RNA m6A位点识别,改进了基因调节的洞察力. 混合方法增强了表表表转录学分析的预测性能.
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.
- 计算生物学 计算生物学
背景情况:
- N6-甲基氨酸 (m6A) 是一种关键的RNA修饰,影响基因调节.
- 鉴定m6A位点是具有挑战性的,因为序列的复杂性和数据的限制.
研究的目的:
- 开发一种准确和可解释的方法来识别RNA m6A修饰部位.
- 通过改进m6A位点预测,增强转录后基因调节分析.
主要方法:
- 开发了一个混合卷积神经网络 (CNN) 和长短期记忆 (LSTM) 框架.
- 为了特征选择和模型可解释性,将Shapley添加式解释 (SHAP) 集成.
- 用生物相关特征编码RNA序列,由CNN处理空间特征,LSTM处理时间依赖.
主要成果:
- 具有SHAP特征选择的CNN-LSTM模型超过了传统和独立的深度学习模型.
- 实现了高性能指标:87.39%的准确度,83.25%的灵敏度,91.52%的特异性和0.7534MCC.
- 证明了用于转录组全方位表转录组学分析的强大潜力.
结论:
- 拟议的CNN-LSTM-SHAP框架准确地对RNA m6A位点进行了分类.
- 该模型为RNA修饰提供了增强的生物洞察力和预测性能.
- 这种方法推动了表表体转录学研究和分析领域的发展.
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