Abstract

Osteoarthritis is a prevalent degenerative joint disease characterized by the breakdown of articular cartilage and significant pain and functional limitations. Despite its high prevalence and impact on individuals' quality of life, effective therapies for OA are limited. Text mining, a subfield of data mining, offers a powerful approach to leverage the vast amount of biomedical literature and accelerate drug discovery in OA. This journal focuses on the advancements and applications of text mining techniques in OA drug discovery, aiming to uncover novel therapeutic targets, identify drug candidates, and understand disease mechanisms. Through the integration of diverse data sources, including scientific articles, clinical trial reports, and genetic databases, text mining enables the extraction and analysis of valuable information. This approach facilitates the identification of potential targets and pathways implicated in OA pathogenesis, the repurposing of existing drugs for OA treatment, and the development of personalized treatment strategies. However, challenges such as data quality and algorithm performance should be addressed. Experimental validation is crucial to ensure the reliability of text mining-based findings. Text mining-based drug discovery in OA holds great promise for transforming the field and accelerating the development of innovative treatments for this debilitating condition.