Predicting Meridian in Chinese traditional medicine using machine learning approaches

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dc.contributor.author Wang, Yinyin
dc.contributor.author Jafari, Mohieddin
dc.contributor.author Tang, Yun
dc.contributor.author Tang, Jing
dc.date.accessioned 2019-12-16T15:38:01Z
dc.date.available 2019-12-16T15:38:01Z
dc.date.issued 2019-11-25
dc.identifier.citation 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
dc.identifier.other PURE: 128454140
dc.identifier.other PURE UUID: e8d413c9-a81b-4e11-9fa7-419eb9ecdf0e
dc.identifier.other RIS: urn:5470D7CD7D49C76A0F2DC085F3EBAB1A
dc.identifier.other ORCID: /0000-0001-7480-7710/work/66366876
dc.identifier.other ORCID: /0000-0002-6991-8587/work/66367009
dc.identifier.other WOS: 000500976100013
dc.identifier.uri http://hdl.handle.net/10138/308414
dc.description.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. en
dc.format.extent 21
dc.language.iso eng
dc.relation.ispartof PLoS Computational Biology
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 3122 Cancers
dc.subject herbs
dc.subject machine learning
dc.subject TRADITIONAL CHINESE MEDICINE
dc.subject IN-SILICO PREDICTION
dc.subject DRUG DISCOVERY
dc.subject NATURAL-PRODUCTS
dc.subject HERBAL MEDICINE
dc.subject SOLUBILITY
dc.subject POLYPHARMACOLOGY
dc.subject EXTRACTION
dc.subject TOOL
dc.subject 1182 Biochemistry, cell and molecular biology
dc.subject 111 Mathematics
dc.title Predicting Meridian in Chinese traditional medicine using machine learning approaches en
dc.type Article
dc.contributor.organization Research Program in Systems Oncology
dc.contributor.organization Faculty of Medicine
dc.contributor.organization University of Helsinki
dc.contributor.organization Institute for Molecular Medicine Finland
dc.contributor.organization Helsinki Institute of Life Science HiLIFE
dc.contributor.organization Faculties
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1371/journal.pcbi.1007249
dc.relation.issn 1553-734X
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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