"令人丧的简单"域名适应跨物种转录因子结合预测
bioRxiv : the preprint server for biology
|June 12, 2025
概括
通过对齐基因组数据分布,MORALE改善了跨物种转录因子 (TF) 结合预测. 这种域调整方法增强了模型概括和动机发现,优于现有的方法.
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:
- 序列到功能模型预测了基因组DNA的功能,但与概括性和解释性作斗争.
- 转录因子 (TF) 结合的跨物种预测通过使用序列变异来增强模型概括性.
- 物种基因组之间的系统差异对跨物种预测构成重大挑战.
研究的目的:
- 开发一个强大的跨物种TF绑定站点预测框架.
- 为了提高序列到函数模型的概括性和解释性.
- 发现跨物种保存的基因调节元件.
主要方法:
- 引入了MORALE,一个使用域调整方法进行跨物种TF约束预测的框架.
- 在没有约束数据的情况下,MORALE在源物种上进行训练,并预测TF对目标物种的约束.
- 调整跨物种数据分布的第一个和第二个时刻,以学习不变序列表示.
主要成果:
- 与基线和梯度逆转 (GRL) 方法相比,MORALE在两种和五种情况下表现出更好的TF结合预测性能.
- MORALE避免了GRL出现的性能下降,并更准确地识别了TF结合动机.
- 在一个五种物种的场景中,MORALE显著改善了人类TF结合部位的预测,超过了只针对人类的模型.
结论:
- MORALE是一种有效和直接的方法,用于使用域调整进行跨物种TF绑定站点预测.
- 该方法通过学习不变的跨物种序列表示来增强模型概括和动机发现.
- 摩拉尔为现有方法提供了有竞争力的替代方案,促进了对跨物种基因调节的理解.
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