Journal
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
Volume 17, Issue -, Pages 282-290Publisher
ELSEVIER
DOI: 10.1016/j.csbj.2019.02.002
Keywords
Network medicine; Herb target prediction; Symptoms; Network embedding
Funding
- National Key Research and Development Program [2017YFC1703506, 2017YFC1700106]
- Fundamental Research Funds for the Central Universities [2017YJS057, 2017JBM020]
- Special Programs of Traditional Chinese Medicine [201407001, JDZX2015170, JDZX2015171]
- National Natural Science Foundation of China [81703945]
- National Key Technology RD Program [2013BAI02B01, 2013BAI13B04]
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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. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
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