Developing the new diagnostic model by integrating bioinformatics and machine learning for osteoarthritis.

ABSTRACT:

Background

Osteoarthritis (OA) is a common cause of disability among the elderly, profoundly affecting quality of life. This study aims to leverage bioinformatics and machine learning to develop an artificial neural network (ANN) model for diagnosing OA, providing new avenues for early diagnosis and treatment.

Methods

From the Gene Expression Omnibus (GEO) database, we first obtained OA synovial tissue microarray datasets. Differentially expressed genes (DEGs) associated with OA were identified through utilization of the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, protein-protein interaction (PPI) network analysis and machine learning were employed to identify the most relevant potential feature genes of OA, and ANN diagnostic model

SUBMITTER: Du J 

PROVIDER: S-EPMC11658098 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature