Multi-Level Association Rule Mining and Network Pharmacology to Identify the Polypharmacological Effects of Herbal Materials and Compounds from Traditional Medicine

Description

An integrated analysis using association rule mining and network pharmacology to identify therapeutic combinations of herbal materials and compounds in traditional medicine. Traditional medicine (TM) has been used to treat a variety of symptoms and diseases through the combination of herbal materials, and it also contributes to the pharmaceutical industry with several advantages such as fewer side effects and significant cost reductions. However, the rules for combining ingredients are not well organized, and complex multi-compound characteristics make it difficult to understand the pharmacological mechanisms among the herbal materials used in TM. In silico approaches that have been proposed to analyze TM and herbal materials require large amount of high-quality structural information or physicochemical properties or have limitations due to ease of interpretation or scope of analysis. In this work, we proposed an approach named InPETM, that integrates association rule mining (ARM) and network pharmacology analyses to identify polypharamcological effects of herbal materials and compounds from TM. Specifically, InPETM performs analyses combining ARM and network pharmacology-based method at the herb-level and compound-level, respectively, and identifies potential herbal material combination and compound candidates for the phenotype. InPETM provided results of pharmacological effects of herbal material combination and compound and identification of mechanism of action in human protein interactome network, which were confirmed by further structural network analysis and literature review analysis. These results indicate that InPETM can contribute to drug development in TM through better understanding of polypharmacological features of herbal materials.

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