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

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 per

SUBMITTER: Wang N 

PROVIDER: S-EPMC6396098 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature