Topic Mining Study on Pharmacological Actions of Ginsenosides Based on Latent Dirichlet Allocation Modeling

doi:10.19894/j.issn.1000-0518.250078

Chinese Journal of Applied Chemistry ›› 2025, Vol. 42 ›› Issue (9): 1289-1296.DOI: 10.19894/j.issn.1000-0518.250078

• Ginseng Column • Previous Articles Next Articles

Topic Mining Study on Pharmacological Actions of Ginsenosides Based on Latent Dirichlet Allocation Modeling

Fang XIA¹, Feng-Jun LI¹, Cheng-Guo LI², Jie REN¹, Lin-Lin LIU¹, Shu-Min YIN¹, You DONG¹()

^1.School of Management，Changchun University of Traditional Chinese Medicine，Changchun 130117，China
^2.Changchun Hongyu Technology Co. ，Ltd. ，Changchun 130507，China

Received:2025-02-26 Accepted:2025-05-20 Published:2025-09-01 Online:2025-09-28
Contact: You DONG
About author:1291433564@qq.com
Supported by:
Special Research on Traditional Chinese Medicine Science and Technology of the State Administration of Traditional Chinese Medicine and the Research Project on the Precision Consultation of Changchun Association for Science and Technology(CCKX-2025-01B)

Abstract

Abstract:

The latent dirichlet allocation （LDA） model was used to analyze the literature on the pharmacological effects of ginsenosides， and the research hotspots and development trends were clarified. A total of 219 papers on the pharmacological effects of ginsenosides were selected from CNKI from 2020 to 2024 using the LDA topic model， and the research topics and their evolution trajectories were analyzed. Five core themes were identified： glycosylation and neurons （Topic 0）， apoptosis and autophagy regulation （Topic 1）， anti-fibrosis and anti-tumor （Topic 2）， anti-inflammatory and antioxidant （Topic 3）， and intestinal inflammation and liver injury （Topic 4） were identified by using the confusion evaluation index and the visual clustering results of pyLDAvis. This paper provides a scientific reference and research direction for further research on the pharmacological effects of ginsenosides， and promotes its application in the field of biomedicine.

Key words: LDA topic model, Ginsenosides, Pharmacological effects, Research hotspots, Development trends

CLC Number:

O629.9

Fang XIA, Feng-Jun LI, Cheng-Guo LI, Jie REN, Lin-Lin LIU, Shu-Min YIN, You DONG. Topic Mining Study on Pharmacological Actions of Ginsenosides Based on Latent Dirichlet Allocation Modeling[J]. Chinese Journal of Applied Chemistry, 2025, 42(9): 1289-1296.

单词	加权百分比
人参	4.02%
皂苷	3.46%
细胞	2.71%
研究	1.49%
作用	1.40%
表达	1.21%

单词	加权百分比
人参	4.02%
皂苷	3.46%
细胞	2.71%
研究	1.49%
作用	1.40%
表达	1.21%

主题编号	主题概括	前20个高概率特征词
Topic0	糖基化与神经元	大鼠，催化，亚基，合成，小鼠，肺癌，糖基化，肝脏，代谢，葡萄糖，炎症，三七，神经元，损伤，通路，血管，海马，氧化应激，线粒体，神经
Topic1	凋亡与自噬调控	凋亡，肥胖，生物，纤维化，生成，自噬，增殖，环糊精，联合，模型，稀有，运动，脑缺血，表达，脂肪，氧化，激素，卵母细胞，筛选，基因，学习
Topic2	抗纤维化与抗肿瘤	脂质体，肿瘤，大鼠，侵袭，纤维化，肝癌，细胞，肺癌，纳米，线虫，小鼠，心肌，自噬，胰腺癌，线粒体，凋亡，抗肿瘤，迁移，转移，睾丸
Topic3	抗炎与抗氧化	线虫，大鼠，小鼠，脓毒症，心肌，睾丸，神经，乳腺癌，炎症，肿瘤，组织，蒸制，迁移，纤维化，细胞，氧化应激，胶质，衰老，增殖，神经元
Topic4	肠道炎症与肝脏损伤	肠道，肿瘤，胶质，大鼠，神经元，小鼠，脂质体，海马，炎症，凋亡，神经，血管，肝癌，损伤，代谢，纤维化，肺癌，抑郁，肝脏，记忆

主题编号	主题概括	前20个高概率特征词
Topic0	糖基化与神经元	大鼠，催化，亚基，合成，小鼠，肺癌，糖基化，肝脏，代谢，葡萄糖，炎症，三七，神经元，损伤，通路，血管，海马，氧化应激，线粒体，神经
Topic1	凋亡与自噬调控	凋亡，肥胖，生物，纤维化，生成，自噬，增殖，环糊精，联合，模型，稀有，运动，脑缺血，表达，脂肪，氧化，激素，卵母细胞，筛选，基因，学习
Topic2	抗纤维化与抗肿瘤	脂质体，肿瘤，大鼠，侵袭，纤维化，肝癌，细胞，肺癌，纳米，线虫，小鼠，心肌，自噬，胰腺癌，线粒体，凋亡，抗肿瘤，迁移，转移，睾丸
Topic3	抗炎与抗氧化	线虫，大鼠，小鼠，脓毒症，心肌，睾丸，神经，乳腺癌，炎症，肿瘤，组织，蒸制，迁移，纤维化，细胞，氧化应激，胶质，衰老，增殖，神经元
Topic4	肠道炎症与肝脏损伤	肠道，肿瘤，胶质，大鼠，神经元，小鼠，脂质体，海马，炎症，凋亡，神经，血管，肝癌，损伤，代谢，纤维化，肺癌，抑郁，肝脏，记忆

文档编号	Topic0	Topic1	Topic2	Topic3	Topic4	所属主题
0	0.049 105	0.000 126	0.000 113	0.002 724	0.947 931	4
1	0.812 836	0.084 068	0.102 106	0.000 502	0.000 487	0
2	0.121 724	0.000 622	0.874 203	0.000 567	0.002 884	2
3	0.000 098	0.000 101	0.000 355	0.000 097	0.999 349	4
4	0.000 549	0.000 612	0.000 544	0.000 556	0.997 738	4
5	0.296 498	0.000 255	0.699 999	0.003 018	0.000 230	2
……	……	……	……	……	……	……
214	0.000 143	0.178 645	0.001 099	0.000 144	0.819 970	4
215	0.000 080	0.000 083	0.994 646	0.000 080	0.005 112	2
216	0.440 428	0.000 139	0.558 104	0.000 377	0.000 953	2
217	0.999 723	0.000 073	0.000 067	0.000 068	0.000 069	0
218	0.026 727	0.029 840	0.823 589	0.000 288	0.119 557	2

Topic Mining Study on Pharmacological Actions of Ginsenosides Based on Latent Dirichlet Allocation Modeling

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