EPI-DynFusion:基于序列特征和动态融合机制的增强剂-促进剂相互作用预测模型
Ao Zhang1, Jianhua Jia1, Mingwei Sun1
1School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China.
Frontiers in genetics
|August 7, 2025
概括
我们开发了EPI-DynFusion,这是一种用于从DNA序列中预测增强剂-促进剂相互作用 (EPI) 的深度学习模型. 我们的模型使用动态特征融合和注意力机制,实现最先进的性能和提高可解释性.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
- 基因法规 基因法规
背景情况:
- 增强剂-促进剂相互作用 (EPI) 对基因表达调节至关重要.
- 传统的EPI检测方法昂贵,缺乏可扩展性.
- 目前的计算模型因简单的融合策略而难以处理复杂的特征关系.
研究的目的:
- 开发一种先进的深度学习模型,仅使用DNA序列数据来预测EPI.
- 在机器学习模型中克服传统特征融合技术的局限性.
- 提高EPI预测的准确性,可解释性和通用性.
主要方法:
- 提出EPI-DynFusion,这是一个集成DNA序列编码,CNN,变压器和BiGRU的深度学习模型.
- 实施了动态特征融合机制,用于特征依赖性的自适应性学习.
- 整合了卷积块注意模块 (CBAM),以专注于信息序列区域.
- 开发了一般的 (EPI-DynFusion-gen) 和细胞特异的 (EPI-DynFusion-best) 模型变体.
主要成果:
- 在六个基准细胞系上评估模型,获得高AUROC得分 (高达96.2%).
- 在精确召回空间中表现出卓越的性能,使用微调的EPI-DynFusion-best模型 (AUPR 83.3%).
- 确认动态融合策略和注意力机制显著提高了预测性能.
结论:
- EPI-DynFusion为基于序列的EPI预测提供了一个强大的,可扩展的框架.
- 该模型通过使用先进的融合和注意力机制来解决现有方法的局限性.
- 取得了最先进的结果,提高了EPI预测中的解释性和通用性.
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