Global knowledge graph of osteoporosis biomarkers based on large language model embeddings and complex network algorithms
Tan Q., Liang W., Liu M., Fan Q., Pan W., Zhou J.
Laboratory Study, published in Front Endocrinol (Lausanne) (2026) — summary generated from the PubMed abstract.
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Laboratory Study
- Journal
- Front Endocrinol (Lausanne) (2026)
- Reported sample size
- —
- Source database
- Europe PMC
- PMID
- 42181175
- PMCID
- PMC13193976
- DOI
- 10.3389/fendo.2026.1776707
Abstract (original English)
Background This study applies advanced artificial intelligence technologies to reconstruct the global research landscape of osteoporosis (OP) biomarkers and to predict emerging nodes with potential clinical value. Methods Literature from the Web of Science Core Collection and PubMed over the past 20 years was integrated. The OpenAI text-embedding-3-large model mapped keywords into a high-dimensional semantic space, enabling deep clustering through cosine similarity and reducing polysemy. The Walktrap community detection algorithm and Callon strategic diagram were used to identify high-centrality, low-density themes in the "fourth quadrant." Gemini 2.5 Pro was applied to generate entity-relation triples, which were validated through a human-in-the-loop process and integrated into a refined knowledge graph. Topological indicators such as betweenness centrality were used to detect emerging potential nodes. Results A total of 1595 core publications were included, showing a significant linear rise in annual output. Nine major research themes were identified. The strategic diagram highlighted three high-potential domains: inflammation and immune regulation, detection technologies and biosensing, and gut microbiota and metabolism. Knowledge-graph analysis revealed oxidative stress as a key bridging node integrating immune, metabolic, and other systems. Several emerging nodes with stro
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
- • This is preclinical work; animal or laboratory results cannot be applied to humans.
Evidence level
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
How we grade evidenceBrowse all related research
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