Multi-drug resistance (MDR) has become one of the greatest threats to human health worldwide, and novel treatment methods of infections caused by MDR bacteria are urgently needed. Phage therapy is a promising alternative to solve this problem, to which the key is correctly matching target pathogenic bacteria with the corresponding therapeutic phage. Deep learning is powerful for mining complex patterns to generate accurate predictions. In this study, we develop PredPHI ( Pred icting P hage- H ost I nteractions), a deep learning-based tool capable of predicting the host of phages from sequence data. We collect >3000 phage-host pairs along with their protein sequences from PhagesDB and GenBank databases and extract a set of features. Then we select high-quality negative samples based on the K-Means clustering method and construct a balanced training set. Finally, we employ a deep convolutional neural network to build the predictive model. The results indicate that PredPHI can achieve a predictive performance of 81 percent in terms of the area under the receiver operating characteristic curve on the test set, and the clustering-based method is significantly more robust than that based on randomly selecting negative samples. These results highlight that PredPHI is a useful and accurate tool for identifying phage-host interactions from sequence data.

中文翻译：

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一种基于深度学习的噬菌体-宿主相互作用识别方法

多重耐药性（MDR）已成为全球人类健康的最大威胁之一，迫切需要新的治疗方法来治疗由MDR细菌引起的感染。噬菌体疗法是​​解决这一问题的有希望的替代方法，其关键是正确匹配目标病原菌与相应的治疗性噬菌体。深度学习对于挖掘复杂模式以生成准确的预测非常有用。在这项研究中，我们开发了 PredPHI ( 预测噬菌体 主持人Interactions)，一种基于深度学习的工具，能够从序列数据中预测噬菌体宿主。我们从 PhagesDB 和 GenBank 数据库中收集超过 3000 个噬菌体-宿主对及其蛋白质序列，并提取一组特征。然后我们基于K-Means聚类方法选择高质量的负样本，构建一个平衡的训练集。最后，我们采用深度卷积神经网络来构建预测模型。结果表明，PredPHI 在测试集上的接收者操作特征曲线下面积方面可以达到 81% 的预测性能，并且基于聚类的方法明显比基于随机选择负样本的方法更稳健。