一个基于DNABERT的深度学习框架,用于预测转录因子结合部位
Pratik Dutta1, Nimisha Ghosh2, Daniele Santoni3
1Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India. pratikdutta@soa.ac.in.
Scientific reports
|February 3, 2026
概括
我们开发了TFBS-Finder,这是一个用于预测转录因子结合站点 (TFBS) 的深度学习模型. 我们的模型优于现有的方法,增强对基因调节的理解.
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:
- 转录因子 (TFs) 通过与称为转录因子结合位 (TFBSs) 的特定DNA序列结合来调节基因表达.
- 准确的TFBS预测对于破译基因调节网络和细胞功能至关重要.
- 现有的TFBS预测深度学习模型提供了改进的空间.
研究的目的:
- 开发一种先进的深度学习模型,TFBS-Finder,用于准确预测转录因子绑定站点.
- 利用预先训练的模型和新的注意力机制,从DNA序列中提取增强的特征.
主要方法:
- TFBS-Finder模型集成了预训练的DNABERT,用于序列嵌入和捕获远程依赖.
- 它包含卷积神经网络 (CNN),修改卷积区注意模块 (MCBAM) 和注意的多尺度卷积 (MSCA),用于局部特征提取.
- 该模型在165个ENCODE ChIP-seq数据集上进行了训练和验证.
主要成果:
- 与现有方法相比,TFBS-Finder在TFBS预测方面表现优越.
- 废除研究和跨细胞系验证证实了该模型的有效性.
- 视觉分析为预测结果的解释性提供了洞察力.
结论:
- 拟议的TFBS-Finder模型在预测转录因子结合站点方面取得了重大进展.
- 它的架构有效地结合了远程和局部特征提取,以提高准确性.
- 该模型的可解释性和卓越性能使其成为基因组研究的宝贵工具.
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