Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network
2019
Wang, Ning | Li, Peng | Hu, Xiaochen | Yang, Kuo | Peng, Y. (Yonghong) | Zhu, Qiang | Zhang, Runshun | Gao, Zhuye | Xu, Hao | Liu, Baoyan | Chen, Jianxin | Zhou, Xuezhong
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.
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