Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
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DOI：
10.24963/ijcai.2018/483
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Tengfei Ma;Cao Xiao;Jiayu Zhou;Fei Wang
中科院分区：
文献类型：
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作者：
Tengfei Ma;Cao Xiao;Jiayu Zhou;Fei Wang
已经研究了药物相似性以支持下游临床任务，例如从已知特性推断药物的新特性（例如，副作用、适应症、相互作用）。新型药物特征的日益可用性带来了学习更全面和准确的药物相似性的机会，这些药物相似性代表了潜在药物关系的全部范围。然而，它是具有挑战性的，整合这些异构的，嘈杂的，非线性相关的信息，学习准确的相似性措施，特别是当标签是稀缺的。此外，在准确性和可解释性之间存在权衡。在本文中，我们建议从多种类型的药物特征中学习准确和可解释的相似性度量。特别是，我们使用多视图图自动编码器对集成进行建模，并添加注意机制来确定每个视图相对于相应任务和功能的权重，以获得更好的可解释性。我们的模型对于半监督和无监督设置都具有灵活的设计。实验结果表明，显着的预测精度提高。案例研究还表明，模型的能力（如嵌入节点功能）和可解释性更好。
Drug similarity has been studied to support downstream clinical tasks such as inferring novel properties of drugs (e.g. side effects, indications, interactions) from known properties. The growing availability of new types of drug features brings the opportunity of learning a more comprehensive and accurate drug similarity that represents the full spectrum of underlying drug relations. However, it is challenging to integrate these heterogeneous, noisy, nonlinear-related information to learn accurate similarity measures especially when labels are scarce. Moreover, there is a trade-off between accuracy and interpretability. In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features. In particular, we model the integration using multi-view graph auto-encoders, and add attentive mechanism to determine the weights for each view with respect to corresponding tasks and features for better interpretability. Our model has flexible design for both semi-supervised and unsupervised settings. Experimental results demonstrated significant predictive accuracy improvement. Case studies also showed better model capacity (e.g. embed node features) and interpretability.