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• W2913843460 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." @default.
• W2913843460 created "2019-02-21" @default.
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• W2913843460 date "2019-01-01" @default.
• W2913843460 modified "2023-10-15" @default.
• W2913843460 title "Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network" @default.
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• W2913843460 doi "https://doi.org/10.1016/j.csbj.2019.02.002" @default.
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