An intrinsically interpretable neural network architecture for sequence to function learning.
An intrinsically interpretable neural network architecture for sequence to function learning.
复制标题
用于序列到函数学习的本质上可解释的神经网络架构。
DOI：
10.1101/2023.01.25.525572
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Chikina,Maria
中科院分区：
文献类型：
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作者：
Balcı,AliTuğrul;Ebeid,MarkMaher;Benos,PanayiotisV;Kostka,Dennis;Chikina,Maria
基于序列的深度学习方法已被证明可以预测多种功能基因组读数，包括开放染色质区域和基因的RNA表达。然而，目前的方法的一个主要局限性是，模型解释依赖于计算上的需求post-analysis，即使这样，人们往往不能解释高度参数化模型的内部机制。在这里，我们介绍了一种深度学习架构，称为完全可解释的序列到功能模型（tiSFM）。tiSFM改进了标准多层卷积模型的性能，同时使用更少的参数。此外，虽然tiSFM本身在技术上是一个多层神经网络，内部模型参数是内在的相关序列motifs.ResultsWe分析发表的开放染色质测量造血谱系细胞类型，并证明tiSFM优于一个国家的最先进的卷积神经网络模型定制这个数据集。我们还表明，它正确地识别了已知在造血分化中发挥作用的转录因子的上下文特异性活动，包括B细胞的Pax 5和Ebf 1，以及先天淋巴细胞的Rorc。tiSFM的模型参数具有生物学意义的解释，我们展示了我们的方法在预测表观遗传状态变化的复杂任务上的实用性，作为发育transition.Availability和implementationThe源代码，包括用于分析关键发现的脚本，可以在https：//github.com/boooooogey/ATAConv上找到，用Python实现。
MotivationSequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demandingpost hocanalyses, and even then, one can often not explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called totally interpretable sequence-to-function model (tiSFM). tiSFM improves upon the performance of standard multilayer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multilayer neural network, internal model parameters are intrinsically interpretable in terms of relevant sequence motifs.ResultsWe analyze published open chromatin measurements across hematopoietic lineage cell-types and demonstrate that tiSFM outperforms a state-of-the-art convolutional neural network model custom-tailored to this dataset. We also show that it correctly identifies context-specific activities of transcription factors with known roles in hematopoietic differentiation, including Pax5 and Ebf1 for B-cells, and Rorc for innate lymphoid cells. tiSFM’s model parameters have biologically meaningful interpretations, and we show the utility of our approach on a complex task of predicting the change in epigenetic state as a function of developmental transition.Availability and implementationThe source code, including scripts for the analysis of key findings, can be found at https://github.com/boooooogey/ATAConv, implemented in Python.