利用Weisfeiler-Lehman神经网络预测药物-靶标相互作用

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) Pub Date : 2019-05-19 DOI:10.1109/BHI.2019.8834572

H. E. Manoochehri, Susmitha Sri Kadiyala, M. Nourani

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引用次数: 4

摘要

在药物研究中，预测缺失的药物-靶标关系有助于加快确定化学药物与靶蛋白之间未知相互作用的过程。本文采用Weisfeiler-Lehman神经网络方法捕捉特征，纯粹基于拓扑网络，学习药物-靶标相互作用的模式。我们表明，我们的方法能够学习复杂的药物靶标拓扑特征，并且在AUROC方面优于其他基于相似性的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Predicting Drug-Target Interactions Using Weisfeiler-Lehman Neural Network

Predicting missing drug-target relationships can help to speed up the process of identifying unknown interactions between chemical drugs and target proteins in pharmaceutical research. In this paper we employ Weisfeiler-Lehman Neural Network method to capture features, purely based on topological network and learn the pattern of drug-target interactions. We show our approach is able to learn sophisticated drug-target topological features and outperform other similarity based methods in terms of AUROC.

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来源期刊

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)

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