Evidence map›Paper›PMID 37889962›Full record

ArticleScience advances2023

Network medicine framework reveals generic herb-symptom effectiveness of traditional Chinese medicine.

Xiao Gan, Zixin Shu, Xinyan Wang, Dengying Yan, Jun Li, Shany Ofaim, Réka Albert, Xiaodong Li, Baoyan Liu, Xuezhong Zhou and 1 more

Abstract read
In one paragraph

Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

73 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Exploring the Therapeutic Effects ofPharmaceuticals (Basel, Switzerland) · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Review
  20. Review

13 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Xiao GanInstitute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China.ORCID 0000-0001-9024-0347
Zixin ShuInstitute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100063, China.ORCID 0000-0001-6078-5645
Xinyan WangInstitute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100063, China.ORCID 0000-0002-1984-5826
Dengying YanInstitute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100063, China.ORCID 0009-0005-7112-6422
Jun LiHubei University of Chinese Medicine, Wuhan 430065, China.ORCID 0009-0000-9864-1671
Shany OfaimNetwork Science Institute, Northeastern University, Boston, MA 02115, USA.ORCID 0000-0001-8641-7007
Réka AlbertDepartment of Physics, Pennsylvania State University, University Park, PA 16802, USA.ORCID 0000-0002-9485-0828
Xiaodong LiHubei University of Chinese Medicine, Wuhan 430065, China.ORCID 0000-0002-6406-9416
Baoyan LiuChina Academy of Chinese Medical Sciences, Beijing 100700, China.ORCID 0000-0002-9677-5366
Xuezhong ZhouInstitute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100063, China.ORCID 0000-0002-4713-3594
Albert-László BarabásiNetwork Science Institute, Northeastern University, Boston, MA 02115, USA.ORCID 0000-0002-4028-3522

Funding

Systems Biology of Airway DiseaseP01HL132825 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI DEMEO, DAWN L · 2016 to 2020
$12.6M
NHLBI NIH HHS P01 HL132825
6 · The paper itself

Abstract

Understanding natural and traditional medicine can lead to world-changing drug discoveries. Despite the therapeutic effectiveness of individual herbs, traditional Chinese medicine (TCM) lacks a scientific foundation and is often considered a myth. In this study, we establish a network medicine framework and reveal the general TCM treatment principle as the topological relationship between disease symptoms and TCM herb targets on the human protein interactome. We find that proteins associated with a symptom form a network module, and the network proximity of an herb's targets to a symptom module is predictive of the herb's effectiveness in treating the symptom. These findings are validated using patient data from a hospital. We highlight the translational value of our framework by predicting herb-symptom treatments with therapeutic potential. Our network medicine framework reveals the scientific foundation of TCM and establishes a paradigm for understanding the molecular basis of natural medicine and predicting disease treatments.

Indexed as

Drugs, Chinese HerbalMedicine, Chinese TraditionalHumansProteinsDrugs, Chinese HerbalProteins

Identifiers

PMID37889962
PMCPMC10610911

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.