Tian Zhen, Yu Yue, Fang Haichuan, Xie Weixin, Guo Maozu
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China.
Institute of Intelligent System and Bioinformatics, College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150000, China.
Brief Bioinform. 2023 Mar 19;24(2). doi: 10.1093/bib/bbac634.
Predicting the associations between human microbes and drugs (MDAs) is one critical step in drug development and precision medicine areas. Since discovering these associations through wet experiments is time-consuming and labor-intensive, computational methods have already been an effective way to tackle this problem. Recently, graph contrastive learning (GCL) approaches have shown great advantages in learning the embeddings of nodes from heterogeneous biological graphs (HBGs). However, most GCL-based approaches don't fully capture the rich structure information in HBGs. Besides, fewer MDA prediction methods could screen out the most informative negative samples for effectively training the classifier. Therefore, it still needs to improve the accuracy of MDA predictions.
In this study, we propose a novel approach that employs the Structure-enhanced Contrastive learning and Self-paced negative sampling strategy for Microbe-Drug Association predictions (SCSMDA). Firstly, SCSMDA constructs the similarity networks of microbes and drugs, as well as their different meta-path-induced networks. Then SCSMDA employs the representations of microbes and drugs learned from meta-path-induced networks to enhance their embeddings learned from the similarity networks by the contrastive learning strategy. After that, we adopt the self-paced negative sampling strategy to select the most informative negative samples to train the MLP classifier. Lastly, SCSMDA predicts the potential microbe-drug associations with the trained MLP classifier. The embeddings of microbes and drugs learning from the similarity networks are enhanced with the contrastive learning strategy, which could obtain their discriminative representations. Extensive results on three public datasets indicate that SCSMDA significantly outperforms other baseline methods on the MDA prediction task. Case studies for two common drugs could further demonstrate the effectiveness of SCSMDA in finding novel MDA associations.
The source code is publicly available on GitHub https://github.com/Yue-Yuu/SCSMDA-master.
预测人类微生物与药物之间的关联（MDA）是药物开发和精准医学领域的关键一步。由于通过湿实验发现这些关联既耗时又费力，计算方法已成为解决此问题的有效途径。最近，图对比学习（GCL）方法在从异质生物图（HBG）中学习节点嵌入方面显示出巨大优势。然而，大多数基于GCL的方法并未充分捕捉HBG中丰富的结构信息。此外，能够筛选出最具信息性的负样本以有效训练分类器的MDA预测方法较少。因此，仍需提高MDA预测的准确性。
在本研究中，我们提出了一种新颖的方法，即采用结构增强对比学习和自步负采样策略进行微生物 - 药物关联预测（SCSMDA）。首先，SCSMDA构建微生物和药物的相似性网络以及它们不同的元路径诱导网络。然后，SCSMDA利用从元路径诱导网络中学到的微生物和药物表示，通过对比学习策略增强从相似性网络中学到的嵌入。之后，我们采用自步负采样策略选择最具信息性的负样本以训练MLP分类器。最后，SCSMDA使用训练好的MLP分类器预测潜在的微生物 - 药物关联。通过对比学习策略增强了从相似性网络中学到的微生物和药物嵌入，从而获得它们的判别性表示。在三个公共数据集上的大量结果表明，SCSMDA在MDA预测任务上显著优于其他基线方法。对两种常见药物的案例研究可以进一步证明SCSMDA在发现新的MDA关联方面的有效性。
源代码可在GitHub上公开获取，网址为https://github.com/Yue-Yuu/SCSMDA-master 。
