iEnhancer-Flow:集成基于变压器的序列学习与DNA形状洞察力,实现强大的增强器预测
Huan Liu1, Hanyu Luo1,2, Lingyun Luo3
1School of Computer Science, University of South China, Hengyang, 421001, China.
Interdisciplinary sciences, computational life sciences
|November 24, 2025
概括
预测增强剂对于基因调节至关重要,通过iEnhancer-Flow得到改善. 这种新的方法整合了DNA序列和形状,优于现有的模型,以更好地理解基因组背景.
科学领域:
- 基因组学就是基因组学.
- 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:
- 增强剂是基因组中重要的非编码调控元素.
- 由于序列的可变性和缺乏明确的动机,预测增强剂具有挑战性.
- 现有的仅序列模型在捕捉监管复杂性方面存在局限性.
研究的目的:
- 开发一个新的框架,iEnhancer-Flow,用于增强增强器分类.
- 整合DNA序列和形状特征,以提高预测准确度.
- 在不同的基因组环境中克服仅序列模型的局限性.
主要方法:
- 提出了iEnhancer-Flow,一个双分支模型,将DNABERT-2用于序列和CNN用于DNA形状.
- 利用中心差异技术捕获局部DNA形状变异.
- 采用流动注意力机制来融合序列和形状特征,然后进行MLP分类.
主要成果:
- 在增强器预测方面,iEnhancer-Flow在竞争方法中表现优越.
- 在多个细胞系中实现了平衡精度 (Bacc) 和马修斯相关系数 (MCC) 的显著改进.
- 在各种生物环境中表现出稳定性和稳健性,在八个细胞系中的六个细胞系中表现优于其他模型.
结论:
- 整合DNA序列和形状特征代表了增强器预测的重大进步.
- iEnhancer-Flow捕获了超越单独序列的互补调节信号.
- 包括序列和结构DNA语境在内的全面观点对于理解基因组调节至关重要.
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