ArticleEvidence-based complementary and alternative medicine : eCAM2022
Bibliometric Analysis of Network Pharmacology in Traditional Chinese Medicine.
Article in Evidence-based complementary and alternative medicine : eCAM, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Therapeutic efficacy and mechanisms of Xuebijing inFrontiers in pharmacology · 2025Pooled it
- Leveraging network pharmacology in the treatment of asthma.ADMET & DMPK · 2026Review
- Silkworm Cocoon-Derived Carbon Dots for Post-Trauma Hemostasis and Tissue Repair.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery.Scientific reports · 2025Article
- Integrating network pharmacology and experimental verification to reveal the ferroptosis-associated mechanism of Changpu-Yizhi-Wan in the treatment of Alzheimer's disease.Metabolic brain disease · 2025Article
- Targeting AGE-RAGE Signaling Pathway with Hujin Decoction Ameliorates MAFLD in HepG2 Cells.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Naringenin modulates oxidative stress and lipid metabolism: Insights from network pharmacology, mendelian randomization, and molecular docking.Frontiers in pharmacology · 2024Article
- Potential bioactive compounds and mechanisms ofFrontiers in aging neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim: We evaluated the developmental process, research status, and existing challenges of network pharmacology. Moreover, we elucidated the corresponding solutions to improve and develop network pharmacology. Methods: Research data for the current study were retrieved from the Web of Science. The developmental process of network pharmacology was analyzed using HisCite, whereas cooccurrence analysis of countries, institutions, keywords, and references in literature was conducted using CiteSpace. Results: In literature, there was a trend of annual increase of studies on network pharmacology and China was found to be the country with the most published literature on network pharmacology. The main publishing research institutions were universities of traditional Chinese medicine (TCM). The keywords with more research frequency were TCM, mechanisms, molecular docking, and quercetin, among others. Conclusion: Currently, studies on network pharmacology are mainly associated with the exploration of action mechanisms of TCM. The main active ingredient in many Chinese medicines is quercetin. This ingredient may lead to deviation of research results, inability to truly analyze active ingredients, and even mislead the research direction of TCM. Such deviation may be because the database fails to reflect the content and composition changes of Chinese medicinal components. The database does not account for interactions among components, targets, and diseases, and it ignores the different pathological states of the disease. Therefore, network pharmacology should be improved from the databases and research methods.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.