ArticleBMC complementary medicine and therapies2024
Predicting herb-disease associations using network-based measures in human protein interactome.
Article in BMC complementary medicine and therapies, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Integrating network pharmacology, molecular docking, machine learning, and experimental validation: puerarin improves sepsis-induced acute kidney injury via the Sirt1-Nrf2-HO-1 pathway.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Predicting circRNA subcellular localization by fusing circRNA sequence and network information.Scientific reports · 2026Article
- Herb-disease association prediction model based on network consistency projection.Scientific reports · 2025Article
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Abstract
backgroundNatural herbs are frequently used to treat diseases or to relieve symptoms in many countries. Moreover, as their safety has been proven for a long time, they are considered as main sources of new drug development. However, in many cases, the herbs are still prescribed relying on ancient records and/or traditional practices without scientific evidences. More importantly, the medicinal efficacy of the herbs has to be evaluated in the perspective of MCMT (multi-compound multi-target) effects, but most efforts focus on identifying and analyzing a single compound experimentally. To overcome these hurdles, computational approaches which are based on the scientific evidences and are able to handle the MCMT effects are needed to predict the herb-disease associations.
resultsIn this study, we proposed a network-based in silico method to predict the herb-disease associations. To this end, we devised a new network-based measure, WACP (weighted average closest path length), which not only quantifies proximity between herb-related genes and disease-related genes but also considers compound compositions of each herb. As a result, we confirmed that our method successfully predicts the herb-disease associations in the human protein interactome (AUROC = 0.777). In addition, we observed that our method is superior than the other simple network-based proximity measures (e.g. average shortest and closest path length). Additionally, we analyzed the associations between Brassica oleracea var. italica and its known associated diseases more specifically as case studies. Finally, based on the prediction results of the WACP, we suggested novel herb-disease pairs which are expected to have potential relations and their literature evidences.
conclusionsThis method could be a promising solution to modernize the use of the natural herbs by providing the scientific evidences about the molecular associations between the herb-related genes targeted by multiple compounds and the disease-related genes in the human protein interactome.
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