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
Document
-
article.pdf - Published Version
Available under License Creative Commons Attribution.
Abstract
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 |
| Related URLs: | |
| SWORD Depositor: | Publication Router |
| Depositing User: | Publication Router |
| URI: | https://sure.sunderland.ac.uk/id/eprint/10682 |
Further Details
More Information
Identifiers
Users with ORCIDS
Catalogue record
Contributors
University Divisions
Actions (login required)
Dimensions
DimensionsDownload Statistics
Downloads per month over past year