ArticleComputational and structural biotechnology journal2019
Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.
Article in Computational and structural biotechnology journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- MDL-HTI: A Multimodal Deep Learning Approach for Predicting Herb-Target Interactions.Interdisciplinary sciences, computational life sciences · 2026Article
- GHF-ACL: A novel contrastive learning framework with multi-order graph structures for herb-disease association prediction.PLoS computational biology · 2026Article
- NovelHTI: An Interpretable Pathway-Enhanced Framework for De Novo Target Prediction of Medicinal Herbs via Cross-Scale Heterogeneous Information Fusion.Pharmaceuticals (Basel, Switzerland) · 2026Article
- TCMToxDB: a comprehensive database for the toxicological analysis of traditional Chinese medicines.Database : the journal of biological databases and curation · 2026Article
- Node2Vec-DGI-EL: a hierarchical graph representation learning model for ingredient-disease association prediction.Bioinformatics advances · 2026Article
- Decoding the anti-thrombotic effects of leonurine: a multimodal approach combining TCM repositioning and mTOR signaling.Chinese medicine · 2025Article
- Herb-disease association prediction model based on network consistency projection.Scientific reports · 2025Article
- Predicting Natural Product-Drug Interactions with Knowledge Graph Embeddings.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model.Scientific reports · 2024Article
- HTINet2: herb-target prediction via knowledge graph embedding and residual-like graph neural network.Briefings in bioinformatics · 2024Article
- Predicting herb-disease associations using network-based measures in human protein interactome.BMC complementary medicine and therapies · 2024Article
- The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data.BioData mining · 2024Article
- Article
- Dual-channel hypergraph convolutional network for predicting herb-disease associations.Briefings in bioinformatics · 2024Article
- Molecular mechanism of the treatment of lung adenocarcinoma byFrontiers in pharmacology · 2024Article
- The OREGANO knowledge graph for computational drug repurposing.Scientific data · 2023Article
- Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine.Briefings in bioinformatics · 2023Review
- Expanding potential targets of herbal chemicals by node2vec based on herb-drug interactions.Chinese medicine · 2023Article
- Effect of botanical drugs in improving symptoms of hypertensive nephropathy: Analysis of real-world data, retrospective cohort, network, and experimental assessment.Frontiers in pharmacology · 2023Article
- Review
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12 authors.
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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.
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