Marker Selection

Marker selection focuses on identifying a minimal, yet highly informative subset of markers from a larger dataset to achieve specific goals, such as improved model performance or enhanced data interpretability. Current research emphasizes the development of sophisticated algorithms, including graph neural networks and generative models, to address challenges like noise reduction, error propagation, and imbalanced data distributions in various applications. These advancements are significantly impacting fields ranging from motion capture and information extraction to single-cell genomics, enabling more accurate analyses, efficient computations, and improved understanding of complex systems.