ArticleBMC medical informatics and decision making2018
Heterogeneous network propagation for herb target identification.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- MDL-HTI: A Multimodal Deep Learning Approach for Predicting Herb-Target Interactions.Interdisciplinary sciences, computational life sciences · 2026Article
- Transfer Learning and Permutation-Invariance Improving Predicting Genome-Wide, Cell-Specific and Directional Interventions Effects of Complex Systems.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Herb-disease association prediction model based on network consistency projection.Scientific reports · 2025Article
- Computational approaches to predict the toxicity of bioactive natural products: a mini review of methodologies.Food science and biotechnology · 2025Review
- 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
- Review
- Heterogeneous network propagation with forward similarity integration to enhance drug-target association prediction.PeerJ. Computer science · 2022Article
- Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.Computational and structural biotechnology journal · 2019Article
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Authors and funding
7 authors.
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Abstract
backgroundIdentifying targets of herbs is a primary step for investigating pharmacological mechanisms of herbal drugs in Traditional Chinese medicine (TCM). Experimental targets identification of herbs is a difficult and time-consuming work. Computational method for identifying herb targets is an efficient approach. However, how to make full use of heterogeneous network data about herbs and targets to improve the performance of herb targets prediction is still a dilemma.
methodsIn our study, a random walk algorithm on the heterogeneous herb-target network (named heNetRW) has been proposed to identify protein targets of herbs. By building a heterogeneous herb-target network involving herbs, targets and their interactions and simulating random walk algorithm on the network, the candidate targets of the given herb can be predicted.
resultsThe experimental results on large-scale dataset showed that heNetRW had higher performance of targets prediction than PRINCE (improved F1-score by 0.08 and Hit@1 by 21.34% in one validation setting, and improved F1-score by 0.54 and Hit@1 by 69.08% in the other validation setting). Furthermore, we evaluated novel candidate targets of two herbs (rhizoma coptidis and turmeric), which showed our approach could generate potential targets that are valuable for further experimental investigations.
conclusionsCompared with PRINCE algorithm, heNetRW algorithm can fuse more known information (such as, known herb-target associations and pathway-based similarities of protein pairs) to improve prediction performance. Experimental results also indicated heNetRW had higher performance than PRINCE. The prediction results not only can be used to guide the selection of candidate targets of herbs, but also help to reveal the molecule mechanisms of herbal drugs.
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