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Updated: May 26, 2026
07:35
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
Global knowledge graph of osteoporosis biomarkers based on large language model embeddings and complex network
Qizhi Tan1, Wenchu Liang2, Manting Liu1
1Guangzhou University of Chinese Medicine, Guangzhou, China.
Frontiers in Endocrinology
|May 25, 2026
Summary
Advanced AI reveals new osteoporosis biomarkers. This study maps research trends, identifying inflammation, gut microbiota, and novel molecular targets for early detection and treatment.
Area of Science:
- Biomedical research
- Artificial Intelligence
- Bibliometrics
Background:
- Osteoporosis (OP) research is evolving beyond single-hormone mechanisms.
- Understanding the complex network of OP biomarkers is crucial for clinical advancement.
Purpose of the Study:
- To reconstruct the global research landscape of osteoporosis (OP) biomarkers using AI.
- To predict emerging OP biomarker nodes with potential clinical value.
Main Methods:
- Utilized Web of Science and PubMed literature (20 years).
- Employed OpenAI text-embedding-3-large for semantic mapping and Gemini 2.5 Pro for knowledge graph construction.
- Applied Walktrap community detection and Callon strategic diagrams to identify key research themes and emerging nodes.
Main Results:
- Identified nine major research themes, highlighting inflammation, immune regulation, detection technologies, biosensing, gut microbiota, and metabolism as high-potential domains.
- Revealed oxidative stress as a central bridging node in OP research.
- Discovered emerging biomarkers (lncRNA MEG3, miR-21-5p, spermidine, Treg-Exo) and imaging innovations (opportunistic CT, bone marrow PDFF).
Conclusions:
- AI and network algorithms offer a systematic approach to uncover latent knowledge in OP biomarker research.
- Identified emerging nodes provide guidance for precision screening (liquid biopsy, radiation-free imaging) and novel therapies (exosomes, gut-bone axis interventions).
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