دانلود مقاله انگلیسی رایگان:کاوش مکانیسم طب سنتی چینی با رویکرد هوش مصنوعی با استفاده از یادگیری ماشین بدون نظارت برای شباهت عملکردی سلولی ترکیبات در شبکه های ناهمگن ، گرانول های XiaoErFuPi به عنوان مثال - 2020
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  • Exploration of the mechanism of traditional Chinese medicine by AI approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, XiaoErFuPi granules as an example Exploration of the mechanism of traditional Chinese medicine by AI approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, XiaoErFuPi granules as an example
    Exploration of the mechanism of traditional Chinese medicine by AI approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, XiaoErFuPi granules as an example

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Exploration of the mechanism of traditional Chinese medicine by AI approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, XiaoErFuPi granules as an example


    ترجمه فارسی عنوان مقاله:

    کاوش مکانیسم طب سنتی چینی با رویکرد هوش مصنوعی با استفاده از یادگیری ماشین بدون نظارت برای شباهت عملکردی سلولی ترکیبات در شبکه های ناهمگن ، گرانول های XiaoErFuPi به عنوان مثال


    منبع:

    Sciencedirect - Elsevier - Pharmacological Research, 160 (2020) 105077. doi:10.1016/j.phrs.2020.105077


    نویسنده:

    Feifei Guoa,1, Xuan Tanga,b,1, Wen Zhanga,c, Junying Weia, Shihuan Tanga,*, Hongwei Wua,*, Hongjun Yanga,d,*


    چکیده انگلیسی:

    ‘Polypharmacology’ is usually used to describe the network-wide effect of a single compound, but traditional Chinese medicine (TCM) has a polypharmacological effect naturally based on the ‘multi-components, multitargets and multi-pathways’ principle. It is a challenge to investigate the polypharmacology mechanism of TCM with multiple components. In this study, we used XiaoErFuPi (XEFP) granules as an example to describe an unsupervised learning strategy for polypharmacology research of TCM and to explore the mechanism of XEFP polypharmacology against multifactorial disease function dyspepsia (FD). Unsupervised clustering of compounds based on similarity evaluation of cellular function fingerprints showed that compounds of TCM without similar targets and chemical structure could also exert similar therapeutic effects on the same disease, as different targets participate in the same pathway closely associated with the pathological process. In this study, we proposed an unsupervised machine learning strategy for exploring the polypharmacology-based mechanism of TCM, utilizing hierarchical clustering based on cellular functional similarity, to establish a connection from the chemical clustering module to cellular function. Meanwhile, FDA-approved drugs against FD were used as references for the mechanism of action (MoA) of FD. First, according to the compound-compound network built by the similarity of cellular function of XEFP compounds and FDA-approved FD drugs, the possible therapeutic function of TCM may represent a known mechanism of FDA-approved drugs. Then, as unsupervised learning, hierarchical clustering of TCM compounds based on cellular function fingerprint similarity could help to classify the compounds into several modules with similar therapeutic functions to investigate the polypharmacology effect of TCM. Furthermore, the integration of quantitative omics data of TCM and approved drugs (from LINCS datasets) provides more quantitative evidence for TCM therapeutic function consistency with approved drugs. A spasmolytic activity experiment was launched to confirm vanillic acid activity to repress smooth muscle contraction; vanillic acid was also predicted to be active compound of XEFP, supporting the accuracy of our strategy. In summary, the approach proposed in this study provides a new unsupervised learning strategy for polypharmacological research investigating TCM by establishing a connection between the compound functional module and drug-activated cellular processes shared with FDA-approved drugs, which may elucidate the unique mechanism of traditional medicine using FDA-approved drugs as references, facilitate the discovery of potential active compounds of TCM and provide new insights into complex diseases.
    Keywords: Polypharmacology | Traditional Chinese medicine | Unsupervised clustering | Cellular function fingerprints | FDA-approved drugs | Functional dyspepsia


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 13
    حجم فایل: 3073 کیلوبایت

    قیمت: رایگان


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