Enhanced Integrated Gradients: improving interpretability of deep learning models using splicing codes as a case study
Enhanced Integrated Gradients: improving interpretability of deep learning models using splicing codes as a case study
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DOI：
10.1186/s13059-020-02055-7
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发表时间：
2020-06-19
期刊：
影响因子：
12.3
通讯作者：
Barash, Yoseph
中科院分区：
文献类型：
--
作者：
Jha, Anupama;Aicher, Joseph K.;Barash, Yoseph
尽管深度学习模型在生物医学领域的成功和快速适应，但它们缺乏可解释性仍然是一个问题。这里，我们介绍增强的积分梯度(EIG)，这是一种识别与特定预测任务相关的重要特征的方法。以RNA剪接预测和数字分类为例，我们证明了EIG改进了原有的集成梯度法，并产生了信息特征集。然后，我们应用EIG将A1CF确定为肝脏特异性选择性剪接的关键调节因子，并通过随后对相关A1CF功能(RNA-seq)和结合数据(PAR-CLIP)的分析支持了这一发现。
Despite the success and fast adaptation of deep learning models in biomedical domains, their lack of interpretability remains an issue. Here, we introduce Enhanced Integrated Gradients (EIG), a method to identify significant features associated with a specific prediction task. Using RNA splicing prediction as well as digit classification as case studies, we demonstrate that EIG improves upon the original Integrated Gradients method and produces sets of informative features. We then apply EIG to identify A1CF as a key regulator of liver-specific alternative splicing, supporting this finding with subsequent analysis of relevant A1CF functional (RNA-seq) and binding data (PAR-CLIP).