论文标题
顺式调节的生物物理模型作为可解释的神经网络
Biophysical models of cis-regulation as interpretable neural networks
论文作者
论文摘要
基因组学中深度学习技术的采用受到机械解释这些技术产生的模型的困难的阻碍。近年来，在基因调节的背景下，已经提出了各种事后归因方法来解决这个神经网络可解释性问题。在这里，我们描述了解决此问题的互补方法。我们的策略基于这样的观察，即顺式调节机制的两种大型生物物理模型可以表示为深度神经网络，其中节点和权重具有明确的生理学解释。我们还展示了如何使用现代深度学习框架从某些类型的大规模平行记者分析（MPRAS）产生的数据来迅速推断出这种生物物理网络。这些结果表明，在广泛的生物学环境中，使用MPRAS系统地表征基因调节的生物物理基础的可扩展策略。他们还强调了基因调节是开发科学解释的深度学习方法的有希望的场所。
The adoption of deep learning techniques in genomics has been hindered by the difficulty of mechanistically interpreting the models that these techniques produce. In recent years, a variety of post-hoc attribution methods have been proposed for addressing this neural network interpretability problem in the context of gene regulation. Here we describe a complementary way of approaching this problem. Our strategy is based on the observation that two large classes of biophysical models of cis-regulatory mechanisms can be expressed as deep neural networks in which nodes and weights have explicit physiochemical interpretations. We also demonstrate how such biophysical networks can be rapidly inferred, using modern deep learning frameworks, from the data produced by certain types of massively parallel reporter assays (MPRAs). These results suggest a scalable strategy for using MPRAs to systematically characterize the biophysical basis of gene regulation in a wide range of biological contexts. They also highlight gene regulation as a promising venue for the development of scientifically interpretable approaches to deep learning.
