用于预测转录因子结合位点的多尺度融合特征的新型双注意力深度神经网络
IEEE journal of biomedical and health informatics
|September 1, 2025
概括
通过融合DNA序列和形状特征,DeepCTMS可以准确预测转录因子结合点 (TFBS). 这种新型的深度学习模型提高了药物发现和疾病治疗的基因调控洞察力.
科学领域:
- 基因组学
- 生物信息学
- 计算生物学
背景情况:
- 转录对于基因调节至关重要,涉及转录因子 (TFs) 与特定的DNA位点 (TFBSs) 结合.
- 精确的TFBS预测有助于理解基因表达,促进药物发现和疾病治疗策略.
- 目前用于TFBS预测的深度学习模型往往忽略了DNA的双螺旋结构和3D形状信息.
研究的目的:
- 推出一个新的深度学习模型DeepCTMS,用于增强TFBS预测.
- 有效地整合DNA序列和3D形状特征以获得更高质量的TFBS表示.
- 解决有关DNA结构信息的现有模型的局限性.
主要方法:
- 开发了DeepCTMS模型,该模型融合了DNA片的序列和形状特征.
- 使用序列特征处理模块捕获DNA双螺旋结构.
- 使用卷积三重注意 (CTA) 模块提取3D DNA形状特征.
- 集成了一个多尺度融合特征处理 (MSFFP) 模块,用于语义特征对齐.
主要成果:
- DeepCTMS有效地融合了序列和形状特征,为TFBS预测提供了卓越的表示.
- 废弃实验证实了DeepCTMS中的单个模块的贡献.
- t-SNE可视化支持该模型能够导出有意义的特征表示.
- 对165个ChIP-seq数据集的跨细胞线验证显示出强大的预测性能和概括性.
结论:
- 在TFBS预测准确性方面,DeepCTMS显著优于现有的基准模型.
- 该模型能够整合多种DNA特征,从而提高预测性能和概括性.
- 深度CTMS为促进基因调节和治疗开发的研究提供了一个有前途的工具.
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