Predicting Meridian in Chinese traditional medicine using machine learning approaches

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Wang , Y , Jafari , M , Tang , Y & Tang , J 2019 , ' Predicting Meridian in Chinese traditional medicine using machine learning approaches ' , PLoS Computational Biology , vol. 15 , no. 11 , 1007249 . https://doi.org/10.1371/journal.pcbi.1007249

Title: Predicting Meridian in Chinese traditional medicine using machine learning approaches
Author: Wang, Yinyin; Jafari, Mohieddin; Tang, Yun; Tang, Jing
Contributor: University of Helsinki, Research Program in Systems Oncology
University of Helsinki, Research Program in Systems Oncology
University of Helsinki, Institute for Molecular Medicine Finland
Date: 2019-11-25
Language: eng
Number of pages: 21
Belongs to series: PLoS Computational Biology
ISSN: 1553-734X
URI: http://hdl.handle.net/10138/308414
Abstract: Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM. Author summary In East Asia, plant-derived natural products, known as herb formulas, have been commonly used as Traditional Chinese Medicine (TCM) for disease prevention and treatment. According to the theory of TCM, herbs can be classified as different Meridians according to the balance of Yin and Yang, which are commonly understood as metaphysical concepts. Therefore, the scientific rational of Meridian classification remains poorly understood. The aim of our study was to provide a computational means to understand the classification of Meridians. We showed that the Meridians of herbs can be predicted by the molecular and chemical features of the ingredient compounds, suggesting that the Meridians indeed are associated with the properties of the compounds. Our work provided a novel chemoinformatics approach which may lead to a more systematic strategy to identify the mechanisms of action and active compounds for TCM herbs.
Subject: 3122 Cancers
herbs
machine learning
TRADITIONAL CHINESE MEDICINE
IN-SILICO PREDICTION
DRUG DISCOVERY
NATURAL-PRODUCTS
HERBAL MEDICINE
SOLUBILITY
POLYPHARMACOLOGY
EXTRACTION
TOOL
1182 Biochemistry, cell and molecular biology
111 Mathematics
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