一个内在可解释的神经网络架构,用于序列到功能学习
Ali Tuğrul Balcı1,2, Mark Maher Ebeid1,2, Panayiotis V Benos3
1Joint Carnegie Mellon University-University of Pittsburgh Program in Computational Biology, Pittsburgh, PA 15213, United States.
Bioinformatics (Oxford, England)
|June 30, 2023
概括
我们为基因组学开发了一种完全可解释的序列到功能模型 (tiSFM). 这种可解释的深度学习模型可以预测功能性基因组读数,性能提高,参数比标准方法少.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 基于序列的深度学习模型预测了基因组功能,但缺乏可解释性.
- 目前的方法需要计算密集的后期分析来解释模型.
- 高度参数化的模型往往会掩盖内部机制.
研究的目的:
- 引入一种新的深度学习架构,即完全可解释的序列到函数模型 (tiSFM).
- 与标准卷积模型相比,提高模型性能和参数效率.
- 允许模型参数与序列图案相关的内在解释.
主要方法:
- 开发了tiSFM深度学习架构.
- 应用tiSFM来分析跨血造细胞类型的开放色素测量.
- 将tiSFM性能与最先进的卷积神经网络进行比较.
主要成果:
- 在开放的染色质数据上,tiSFM在量身定制的卷积神经网络上表现出卓越的性能.
- 确定了对血液形成分化至关重要的特定背景转录因子活动 (例如,Pax5,Ebf1,Rorc).
- tiSFM参数为预测发育过程中的表观遗传状态变化提供了生物学上有意义的解释.
结论:
- tiSFM为基因组学中的标准深度学习模型提供了一个可解释的替代方案.
- 该模型的可解释参数为对序列功能关系的生物学洞察提供了便利.
- tiSFM对于复杂的任务是有效的,比如预测发育表观遗传过渡.
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