Integrating multi-omics, machine learning, and molecular dynamics simulations to identify glutamate metabolism-related biomarkers and drug candidates in rheumatoid arthritis.

Saved in:
Bibliographic Details
Title: Integrating multi-omics, machine learning, and molecular dynamics simulations to identify glutamate metabolism-related biomarkers and drug candidates in rheumatoid arthritis.
Authors: Zhu B; Department of Minimally Invasive Orthopedics, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China., Li B; Department of Minimally Invasive Orthopedics, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China., Zhang S; The First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China., Qi W; The First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China., Mu Z; The First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China., Kong P; Department of Minimally Invasive Orthopedics, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China., Han Y; Department of Minimally Invasive Orthopedics, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China., Shi Z; The First Clinical Medical School, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Source: Frontiers in molecular biosciences [Front Mol Biosci] 2026 Apr 29; Vol. 13, pp. 1834429. Date of Electronic Publication: 2026 Apr 29 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Media S.A Country of Publication: Switzerland NLM ID: 101653173 Publication Model: eCollection Cited Medium: Print ISSN: 2296-889X (Print) Linking ISSN: 2296889X NLM ISO Abbreviation: Front Mol Biosci Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Full text is not displayed to guests.
Description
ISSN:2296-889X
DOI:10.3389/fmolb.2026.1834429