The use of machine learning algorithms in the identification of novel biomarkers for disorders of the immune system

Identifying biomarkers is critical for understanding the pathophysiology, diagnosis, and treatment of immune system disorders. Traditional biomarker discovery methods often face limitations in regard to sensitivity, scalability, and complexity. This review examines the transformative role of machine learning algorithms in addressing these challenges by uncovering novel biomarkers from high-dimensional datasets. Key approaches such as supervised, unsupervised, and semi-supervised learning are highlighted, emphasizing their ability to discern intricate patterns and relationships among molecular features. The studies reviewed underscore the potential of algorithms like random forest, support vector machines, and clustering techniques in biomarker discovery, offering insights into immune system regulation and personalized medicine. Despite promising advances, challenges such as algorithm validation and integration into clinical workflow remain. This review advocates for continued development and standardization to enable machine learning-driven biomarker identification to reshape diagnostics and therapeutics in immunology.