Discovery and Validation of Traditional Chinese and Western Medicine Combination Antirheumatoid Arthritis Drugs Based on Machine Learning (Random Forest Model).

The combination of traditional Chinese medicine (TCM) and Western medicine is a promising method for treating rheumatoid arthritis (RA). Combining the two fully exploits the advantages of Western and TCM to treat RA and has the potential to greatly improve the therapeutic effect on RA. In this study...

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Publicado en:BioMed Research International pp. 1 - 17
Autores principales: Sun, Jijia, Ni, Qinghua, Jiang, Fengyan, Liu, Baocheng, Wang, Jianying, Zhang, Lei, Huang, Jihan
Formato: equations & formulas research tables/charts Journal Article
Publicado: Wiley-Blackwell 2/15/2023
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 2/15/2023
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2023/6086388
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        atl: Discovery and Validation of Traditional Chinese and Western Medicine Combination Antirheumatoid Arthritis Drugs Based on Machine Learning (Random Forest Model).
      aug:
        au:
          Sun, Jijia
          Ni, Qinghua
          Jiang, Fengyan
          Liu, Baocheng
          Wang, Jianying
          Zhang, Lei
          Huang, Jihan
        affil: Shanghai Collaborative Innovation Center of Traditional Chinese Medicine Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
      sug:
        subj:
          Machine Learning
          Random Forest
          Medicine, Chinese Traditional
          Allopathic Medicine
          Antirheumatic Agents Therapeutic Use
          Drug Therapy, Combination
          Arthritis Drug Therapy
          Human
          Funding Source
      ab: The combination of traditional Chinese medicine (TCM) and Western medicine is a promising method for treating rheumatoid arthritis (RA). Combining the two fully exploits the advantages of Western and TCM to treat RA and has the potential to greatly improve the therapeutic effect on RA. In this study, we developed a combination drug training set by using 16 characteristic variables based on the characteristics of small molecules of TCM ingredients and Food and Drug Administration-certified combination drug data downloaded from the DrugCombDB database. Furthermore, we compared the prediction and classification abilities of five models: the k -nearest neighbors, naive Bayes, support vector machine, random forest, and AdaBoost algorithms. The random forest model was selected as the classification and prediction model for Western and TCM and Western combination drugs. We collected data for 41 small molecules of TCM ingredients from the Traditional Chinese Medicine Systems Pharmacology database and 10 small molecule drugs commonly used in anti-RA treatment from the DrugBank database. Combinations of Western and TCM for anti-RA treatment were screened. Finally, the CellTiter-Glo method was used to determine the synergy of these combinations, and the 15 most predicted drug combinations were carried out experimental verification. Myricetin, rhein, nobiletin, and fisetin had high synergy with celecoxib, and rhein had high synergy with hydroxychloroquine. The preliminary findings of this study can be further applied for practical clinical anti-RA combined treatment strategies and serve as a reference for clinical treatment of RA with integrated Western and TCM.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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