Wang Ning, Li Peng, Hu Xiaochen, Yang Kuo, Peng Yonghong, Zhu Qiang, Zhang Runshun, Gao Zhuye, Xu Hao, Liu Baoyan, Chen Jianxin, Zhou Xuezhong
School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China.
College of Arts and Sciences, Shanxi Agricultural University, Taigu 030801, China.
Comput Struct Biotechnol J. 2019 Feb 8;17:282-290. doi: 10.1016/j.csbj.2019.02.002. eCollection 2019.
Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous network, and then performs supervised learning based on these low-dimensional feature representations. HTINet obtains performance improvement over a well-established random walk based herb-target prediction method. Furthermore, we have manually validated several predicted herb-target interactions from independent literatures. These results indicate that HTINet can be used to integrate heterogeneous information to predict novel herb-target interactions.
作为现代医学的补充方法或替代方法，传统中医（TCM）受到了越来越多的关注。然而，识别中药新药靶点的实验方法严重依赖于当前可用的草药-化合物-靶点关系。在这项工作中，我们提出了一种草药-靶点相互作用网络（HTINet）方法，这是一种主要依赖于症状相关关联的用于草药-靶点预测的新型网络集成管道。HTINet专注于通过网络嵌入捕获草药和蛋白质的低维特征向量，该方法整合了跨多层异构网络的节点拓扑属性，然后基于这些低维特征表示进行监督学习。与一种成熟的基于随机游走的草药-靶点预测方法相比，HTINet的性能有所提高。此外，我们从独立文献中人工验证了几个预测的草药-靶点相互作用。这些结果表明，HTINet可用于整合异构信息以预测新的草药-靶点相互作用。
