<ul><li>Gene selection in high-dimensional genomic data is crucial for understanding diseases and improving therapeutic outcomes.</li><li>Traditional feature selection methods often overlook biological pathways and regulatory networks, resulting in unstable and irrelevant gene signatures.</li><li>A new two-stage framework that combines statistical selection with biological pathway knowledge using multi-agent reinforcement learning (MARL) has been introduced.</li><li>Experiments on various gene expression datasets show that this approach enhances prediction accuracy and biological interpretability compared to conventional methods.</li></ul>

Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning

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