ArticleBriefings in bioinformatics2025
Multi-level association rule mining and network pharmacology to identify the polypharmacological effects of herbal materials and compounds in traditional medicine.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Interpreting the Black Box: Interpretable Machine Learning and Systems Pharmacology in Small-Molecule Therapeutics.Pharmaceutics · 2026Review
- Beyond metabolism and blood flow: toward an inflammation-metabolism axis paradigm for herbal medicine in obesity-related coronary heart disease.Frontiers in cardiovascular medicine · 2026Article
- From functional foods to immunotherapeutic agents: mechanistic insights into medicinal mushroom bioactives in chronic inflammation management.Frontiers in nutrition · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Many cultures worldwide have widely used traditional medicine (TM) to prevent or treat diseases. Herbal materials and their compounds used in TM offer many advantages for drug discovery, including cost-effectiveness, fewer side effects, and improved metabolism. However, the multi-compound and multi-target characteristics of TM prescriptions complicate drug discovery; meanwhile, previous studies have been limited by a lack of high-quality data, complex interpretation, and/or narrow analytical ranges. Thus, this study proposed a framework to identify potential therapeutic combinations of herbal materials and their compounds currently used in TM by integrating association rule mining (ARM) and network pharmacology analysis across multiple TM and biological levels. Subsequently, we collected prescriptions, herbal materials, compounds, genes, phenotypes, and all ensuing interactions to identify effective combinations of herbal materials and compounds using ARM for various symptoms and diseases. This proposed analytical approach was also applied to screen effective herbal material combinations and compounds for five phenotypes: asthma, diabetes, arthritis, stroke, and inflammation. The potential pharmacological effects of the inferred candidates were identified at the molecular level using structural network analysis and a literature review. In addition, compounds from Morus alba, Ephedra sinica, Perilla frutescens, and Pinellia ternata, which were strongly associated with asthma, were validated in vitro. Collectively, our study provides ethnopharmacological and biological evidence for the polypharmacological effects of herbal materials and their compounds, thus enhancing the understanding of the mechanisms involved in TM and suggesting potential candidates for prescriptions, dietary supplements, and drug combinations. The source code and results are available at https://github.com/bmil-jnu/InPETM.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.