多视图深度学习框架用于精确预测转录因子绑定站点
Yiben Lin1,2, Huiliang Luo2, Liang Yan3
1Key Laboratory of Micro-nano Sensing and IoT of Wenzhou, Wenzhou Institute of Hangzhou Dianzi University, Wenzhou 325038, China.
Journal of chemical information and modeling
|September 7, 2025
概括
我们开发了一种新的多视图深度学习模型MDNet-TFP, 它通过考虑DNA显著提高了准确性
科学领域:
- 基因组学和分子生物学
- 计算生物学和生物信息学
- 系统生物学
背景情况:
- 转录因子 (TFs) 通过结合特定的DNA位点 (TFBSs) 来调节基因表达.
- 准确的TFBS预测对于理解基因调节,疾病机制和合成生物学至关重要.
- 目前的机器学习方法在模拟DNA结构,远程依赖性和整合多样化的数据方面面临挑战.
研究的目的:
- 为增强转录因子绑定预测 (TFBS预测) 开发先进的深度学习框架.
- 通过结合多个数据视图和DNA序列属性来解决现有模型的局限性.
- 提高TFBS预测模型的准确性和可解释性.
主要方法:
- 提出了MDNet-TFP,这是TFBS预测的多视图深度学习模型.
- 引入了双向反向补充模块 (BiRC-Mamba) 来捕获DNA序列属性.
- 为特征提取和数据集成开发了一种多尺度卷积循环注意网络 (MCRAN).
主要成果:
- 在165个ChIP-seq数据集中,MDNet-TFP实现了卓越的性能,平均ACC为88.13%,ROC-AUC为93.72%,PR-AUC为93.40%.
- 该模型在690个ChIP-seq数据集中显示出高性能.
- 动机可视化证实模型的注意力与已知的TFBS动机一致,表明生物相关性.
结论:
- MDNet-TFP有效地解决了当前TFBS预测方法的局限性.
- 该模型为基因组数据分析提供了更高的准确性,可解释性和概括性.
- 这项工作促进了转录调节的研究,并对生物医学应用产生影响.
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