Improving Automated Feature Selection in Cancer Genomics Using Reinforcement Learning

Genomic datasets are often characterized by high dimensionality and a limited number of samples, making it difficult to identify biologically meaningful features. Effective feature selection is crucial in biomedical research, where both genomic and clinical variables are abundant. In this study, we evaluate multiple feature selection techniques on large-scale gene expression datasets from breast cancer, lung adenocarcinoma, and ovarian cancer. We specifically adapt and optimize a novel reinforcement learning (RL) approach to address the challenges of high-dimensional genomic data. The performance of the RL-based method is compared against widely-used techniques, including Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination (RFE), Random Forest, and Principal Component Analysis (PCA). Our experimental results demonstrate that the RL approach outperforms traditional methods in predicting cancer outcomes in most cases, highlighting its potential for identifying biologically meaningful features in genomic analysis.