通过对抗性训练对一种新型核酸水平深度神经网络进行转录因子结合的跨物种预测
Qinhu Zhang1,2,3, Siguo Wang1, Zhipeng Li1
1Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, 315201, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|July 30, 2024
概括
这项研究引入了一种新的深度学习模型,用于预测跨物种的转录因子 (TF) 结合点. 对抗性训练进一步提高了跨物种预测的准确性和TF约束区域的识别.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 由于结合点的快速演变,预测跨物种的转录因子 (TF) 结合具有挑战性.
- 现有的方法往往显示较低的跨物种预测性能与物种内预测相比.
研究的目的:
- 开发一种新的深度学习模型,用于精确的TF结合预测物种内部和跨物种.
- 通过对抗性培训改进跨物种TF约束预测.
主要方法:
- 开发了一个核酸水平深度神经网络 (NLDNN),将TF结合预测视为核酸水平回归任务.
- NLDNN从DNA序列直接预测实验覆盖值.
- 设计了一个双路径框架,用于对抗训练,以使人类和小鼠物种的域空间保持一致.
主要成果:
- 在TF结合预测,区域定位和区分TF特异单核酸多态 (SNP) 方面,NLDNN的表现优于竞争中的方法.
- 对抗性训练显著改善了人类和小鼠之间的跨物种预测性能.
- 对抗性培训增强了该模型定位TF约束区域和歧视TF特定SNP的能力.
结论:
- 拟议的NLDNN模型为TF绑定预测提供了一个强大的方法.
- 敌对训练是提高跨物种TF结合预测和相关基因组任务的有效策略.
- 模型可视化显示了错误预测的纠正,提高了整体准确性.
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