Volume 15 Issue 6
Jun.  2025
Turn off MathJax
Article Contents
Wenke Xiao, Mengqing Zhang, Danni Zhao, Fanbo Meng, Qiang Tang, Lianjiang Hu, Hongguo Chen, Yixi Xu, Qianqian Tian, Mingrui Li, Guiyang Zhang, Liang Leng, Shilin Chen, Chi Song, Wei Chen. TCMKD: From ancient wisdom to modern insights-A comprehensive platform for traditional Chinese medicine knowledge discovery[J]. Journal of Pharmaceutical Analysis, 2025, 15(6): 101297. doi: 10.1016/j.jpha.2025.101297
Citation: Wenke Xiao, Mengqing Zhang, Danni Zhao, Fanbo Meng, Qiang Tang, Lianjiang Hu, Hongguo Chen, Yixi Xu, Qianqian Tian, Mingrui Li, Guiyang Zhang, Liang Leng, Shilin Chen, Chi Song, Wei Chen. TCMKD: From ancient wisdom to modern insights-A comprehensive platform for traditional Chinese medicine knowledge discovery[J]. Journal of Pharmaceutical Analysis, 2025, 15(6): 101297. doi: 10.1016/j.jpha.2025.101297

TCMKD: From ancient wisdom to modern insights-A comprehensive platform for traditional Chinese medicine knowledge discovery

doi: 10.1016/j.jpha.2025.101297
Funds:

This work was supported by Natural Science Foundation of Sichuan, China (Grant No.: 2024ZDZX0019).

  • Received Date: Dec. 19, 2024
  • Accepted Date: Apr. 04, 2025
  • Rev Recd Date: Mar. 19, 2025
  • Publish Date: Apr. 10, 2025
  • Traditional Chinese medicine (TCM) serves as a treasure trove of ancient knowledge, holding a crucial position in the medical field. However, the exploration of TCM's extensive information has been hindered by challenges related to data standardization, completeness, and accuracy, primarily due to the decentralized distribution of TCM resources. To address these issues, we developed a platform for TCM knowledge discovery (TCMKD, https://cbcb.cdutcm.edu.cn/TCMKD/). Seven types of data, including syndromes, formulas, Chinese patent drugs (CPDs), Chinese medicinal materials (CMMs), ingredients, targets, and diseases, were manually proofread and consolidated within TCMKD. To strengthen the integration of TCM with modern medicine, TCMKD employs analytical methods such as TCM data mining, enrichment analysis, and network localization and separation. These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights. In addition to its analytical capabilities, a quick question and answer (Q&A) system is also embedded within TCMKD to query the database efficiently, thereby improving the interactivity of the platform. The platform also provides a TCM text annotation tool, offering a simple and efficient method for TCM text mining. Overall, TCMKD not only has the potential to become a pivotal repository for TCM, delving into the pharmacological foundations of TCM treatments, but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems, extending beyond just TCM.

  • loading
  • [1]
    X. Lu, M. Peng, J. Zhang, et al., Whole-course management of traditional Chinese medicine in the treatment of EGFRIs related skin toxicity in lung cancer: A series of clinical studies, J. Clin. Oncol. 41 (2023), e21047.
    [2]
    M. Huang, Y. Liu, K. Xiong, et al., The role and advantage of traditional Chinese medicine in the prevention and treatment of COVID-19, J. Integr. Med. 21 (2023) 407-412.
    [3]
    K.K. Chen, A pharmacognostic and chemical study of ma huang (Ephedra vulgaris var. helvetica). 1925, J. Am. Pharm. Assoc. (2003) 52 (2012) 406-412.
    [4]
    M. Rondanelli, A. Riva, G. Petrangolini, et al., Berberine Phospholipid Is an Effective Insulin Sensitizer and Improves Metabolic and Hormonal Disorders in Women with Polycystic Ovary Syndrome: A One-Group Pretest-Post-Test Explanatory Study, Nutrients 13 (2021), 3665.
    [5]
    B. Pang, X. Yu, Q. Zhou, et al., Effect of Rhizoma coptidis (Huang Lian) on Treating Diabetes Mellitus, Evid. Based Complement. Alternat. Med. 2015 (2015), 921416.
    [6]
    Y. Tu, The discovery of artemisinin (qinghaosu) and gifts from Chinese medicine, Nat. Med. 17 (2011) 1217-1220.
    [7]
    Y. Wu, F. Zhang, K. Yang, et al., SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping, Nucleic Acids Res. 47 (2019) D1110-D1117.
    [8]
    Y. Zhang, X. Li, Y. Shi, et al., ETCM v2.0: An update with comprehensive resource and rich annotations for traditional Chinese medicine, Acta Pharm. Sin. B 13 (2023) 2559-2571.
    [9]
    L. Zhang, J. Dong, H. Wei, et al., TCMSID: a simplified integrated database for drug discovery from traditional chinese medicine, J. Cheminform. 14 (2022), 89.
    [10]
    Q. Lv, G. Chen, H. He, et al., TCMBank: bridges between the largest herbal medicines, chemical ingredients, target proteins, and associated diseases with intelligence text mining, Chem. Sci. 14 (2023) 10684-10701.
    [11]
    X. Li, Q. Tang, F. Meng, et al., INPUT: An intelligent network pharmacology platform unique for traditional Chinese medicine, Comput. Struct. Biotechnol. J. 20 (2022) 1345-1351.
    [12]
    M. Fan, C. Jin, D. Li, et al., Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review, Front. Pharmacol. 14 (2023), 1289901.
    [13]
    Y. Liu, J. Xu, Z. Yu, et al., Ontology characterization, enrichment analysis, and similarity calculation-based evaluation of disease-syndrome-formula associations by applying SoFDA, Imeta 2 (2023), e80.
    [14]
    T. Zhang, Z. Huang, Y. Wang, et al., Information Extraction from the Text Data on Traditional Chinese Medicine: A Review on Tasks, Challenges, and Methods from 2010 to 2021, Evid. Based Complement. Alternat. Med. 2022 (2022), 1679589.
    [15]
    W. Guo, H. Jiang, Y. Li, et al., Analysis of Medication Rules of Traditional Chinese Medicine for Malaria Treatment Based on Data Mining, Chin. Med. Sci. J. 38 (2023) 218-227.
    [16]
    X. Wang, F. Pang, X. Du, Analysis of Traditional Chinese Medicine Symptoms in Children with Spastic Cerebral Palsy: A Data Mining Study, J. Multidiscip. Healthc. 17 (2024) 913-922.
    [17]
    S. Ma, L. Zheng, L. Zheng, et al., Data Mining, Network Pharmacology, and Molecular Docking Explore the Effects of Core Traditional Chinese Medicine Prescriptions in Patients with Rectal Cancer and Qi and Blood Deficiency Syndrome, Evid. Based Complement. Alternat. Med. 2021 (2021), 1353674.
    [18]
    Y Zhang, Y Hao, Traditional Chinese Medicine Knowledge Graph Construction Based on Large Language Models, Electronics 13 (2024), 1395.
    [19]
    J. Menche, A. Sharma, M. Kitsak, et al., Disease networks. Uncovering disease-disease relationships through the incomplete interactome, Science 347 (2015), 1257601.
    [20]
    D. Zhang, X. Zhang, B. Peng, et al., Network pharmacology suggests biochemical rationale for treating COVID-19 symptoms with a Traditional Chinese Medicine, Commun. Biol. 3 (2020), 466.
    [21]
    K. Xi, M. Zhang, M. Li, et al., Unveiling the mechanisms of nephrotoxicity caused by nephrotoxic compounds using toxicological network analysis, Mol. Ther. Nucleic Acids 34 (2023), 102075.
    [22]
    S. Tian, J. Zhang, S. Yuan, et al., Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM, Brief. Bioinform. 24 (2023), bbad027.
    [23]
    S. Fang, L. Dong, L. Liu, et al., HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine, Nucleic Acids Res. 49 (2021) D1197-D1206.
    [24]
    J. Ru, P. Li, J. Wang, et al., TCMSP: a database of systems pharmacology for drug discovery from herbal medicines, J. Cheminform. 6 (2014), 13.
    [25]
    D. Yan, G. Zheng, C. Wang, et al., HIT 2.0: an enhanced platform for Herbal Ingredients' Targets, Nucleic Acids Res. 50 (2022) D1238-D1243.
    [26]
    S.K. Kim, M.K. Lee, H. Jang, et al., TM-MC 2.0: an enhanced chemical database of medicinal materials in Northeast Asian traditional medicine, BMC Complement. Med. Ther. 24 (2024), 40.
    [27]
    J.F. Wang, H. Zhou, L.Y. Han, et al., Traditional Chinese medicine information database, Clin. Pharmacol. Ther. 78 (2005) 92-93.
    [28]
    L. Ren, Y. Xu, L. Ning, et al., TCM2COVID: A resource of anti-COVID-19 traditional Chinese medicine with effects and mechanisms, Imeta 1 (2022), e42.
    [29]
    Z. Liu, C. Cai, J. Du, et al., TCMIO: Comprehensive Database of Traditional Chinese Medicine on Immuno-Oncology, Front. Pharmacol. 11 (2020), 439.
    [30]
    G. Richard Bickerton, G.V. Paolini, J. Besnard, et al., Quantifying the chemical beauty of drugs, Nat. Chem. 4 (2012) 90-98.
    [31]
    B.C. Doak, J. Zheng, D. Dobritzsch, et al., How Beyond Rule of 5 Drugs and Clinical Candidates Bind to Their Targets, J. Med. Chem. 59 (2016) 2312-2327.
    [32]
    M.K. Gilson, T. Liu, M. Baitaluk, et al., BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology, Nucleic Acids Res. 44 (2016) D1045-D1053.
    [33]
    P. Jaccard, THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1, New Phytol. 11 (1912) 37-50.
    [34]
    Y. Chen, Y. Hu, X. Hu, et al., CoGO: a contrastive learning framework to predict disease similarity based on gene network and ontology structure, Bioinformatics 38 (2022) 4380-4386.
    [35]
    A. Subramanian, P. Tamayo, V.K. Mootha, et al., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles, Proc. Natl. Acad. Sci. USA 102 (2005) 15545-15550.
    [36]
    D. Dhinakaran, P.M. Joe Prathap, Protection of data privacy from vulnerability using two-fish technique with Apriori algorithm in data mining, J. Supercomput. 78 (2022) 17559-17593.
    [37]
    A.S. Himmel, H. Molter, R. Niedermeier, et al., Adapting the Bron-Kerbosch algorithm for enumerating maximal cliques in temporal graphs, Soc. Netw. Anal. Min. 7 (2017), 35.
    [38]
    Y. He, F. Miao, Y. Fan, et al., Analysis of Acupoint Selection and Combinations in Acupuncture Treatment of Carpal Tunnel Syndrome: A Protocol for Data Mining, J. Pain Res. 16 (2023) 1941-1948.
    [39]
    C. Yang, S.J. Yen, X.D. Chiu, et al., Decision Tree-Based Body Constitution Diagnosis System for Traditional Chinese Medicine, Evid. Based Complement. Alternat. Med. 2022 (2022), 5560087.
    [40]
    Z. Fang, X. Liu, G. Peltz, GSEApy: a comprehensive package for performing gene set enrichment analysis in Python, Bioinformatics 39 (2023), btac757.
    [41]
    G. Yu, L. Wang, Y. Han, et al., clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters, OMICS 16 (2012) 284-287.
    [42]
    E. Guney, J. Menche, M. Vidal, et al., Network-based in silico drug efficacy screening, Nat. Commun. 7 (2016), 10331.
    [43]
    D. Morselli Gysi, I. do Valle, M. Zitnik, et al., Network medicine framework for identifying drug-repurposing opportunities for COVID-19, Proc. Natl. Acad. Sci. USA 118 (2021), e2025581118.
    [44]
    J.Pokorny, Integration of Relational and Graph Databases Functionally, Found. Comput. Decis. Sci. 44 (2019) 427-441.
    [45]
    L. Shi, W. Zhu, Y. Huang, et al., Cancer-associated fibroblast-derived exosomal microRNA-20a suppresses the PTEN/PI3K-AKT pathway to promote the progression and chemoresistance of non-small cell lung cancer, Clin. Transl. Med. 12 (2022), e989.
    [46]
    J. Yu, L. Zhang, J. Peng, et al., Dictamnine, a novel c-Met inhibitor, suppresses the proliferation of lung cancer cells by downregulating the PI3K/AKT/mTOR and MAPK signaling pathways, Biochem. Pharmacol. 195 (2022), 114864.
    [47]
    A. Aburima, M. Berger, B.E.J. Spurgeon, et al., Thrombospondin-1 promotes hemostasis through modulation of cAMP signaling in blood platelets, Blood 137 (2021) 678-689.
    [48]
    Y.J. Choi, E. Williams, M.J. Dahl, et al., Antenatal creatine supplementation reduces persistent fetal lung inflammation and oxidative stress in an ovine model of chorioamnionitis, Am. J. Physiol. Lung Cell. Mol. Physiol. 327 (2024) L40-L53.
    [49]
    Y. Yao, C. Chen, J. Wang, et al., Circular RNA circATP9A promotes non-small cell lung cancer progression by interacting with HuR and by promoting extracellular vesicles-mediated macrophage M2 polarization, J. Exp. Clin. Cancer Res. 42 (2023), 330.
    [50]
    H. Deng, Y. Chen, L. Wang, et al., PI3K/mTOR inhibitors promote G6PD autophagic degradation and exacerbate oxidative stress damage to radiosensitize small cell lung cancer, Cell Death Dis. 14 (2023), 652.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(1)

    Article Metrics

    Article views (205) PDF downloads(15) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return