Gene Selection

Gene selection aims to identify the most informative genes from high-dimensional datasets, improving the efficiency and accuracy of downstream analyses. Current research focuses on integrating advanced machine learning techniques, such as transformer networks and reinforcement learning, with established methods like sparsity-inducing algorithms and feature pre-filtering to optimize gene selection for various applications. This work is crucial for diverse fields, including oncology (improving cancer diagnosis and treatment), crop breeding (enhancing food production), and single-cell genomics (facilitating more precise biological interpretations), ultimately leading to more efficient and effective data analysis.