Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.

Wang, Ning, Li, Peng, Hu, Xiaochen, Yang, Kuo, Peng, Yonghong, Zhu, Qiang, Zhang, Runshun, Gao, Zhuye, Xu, Hao, Liu, Baoyan, Chen, Jianxin and Zhou, Xuezhong (2019) Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network. Computational and structural biotechnology journal, 17. pp. 282-290. ISSN 2001-0370

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Abstract

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.

Item Type: Article
Additional Information: ** From Europe PMC via Jisc Publications Router ** History: ppub 01-01-2019; epub 08-02-2019. ** Licence for this article: cc by
Uncontrolled Keywords: Symptoms, Network Medicine, Network Embedding, Herb Target Prediction
Divisions: Faculty of Technology > School of Computer Science
Related URLs:
SWORD Depositor: Publication Router
Depositing User: Publication Router
Date Deposited: 10 Mar 2020 11:05
Last Modified: 10 Mar 2020 11:05
URI: http://sure.sunderland.ac.uk/id/eprint/10682

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