Reinforcement Learning for Senescence Biomarker Discovery

Problem

Identifying minimal gene panels that distinguish senescent cells from healthy cells is challenging due to high-dimensional data and complex cellular states in unlabeled data.

Approach

I built a reinforcement learning framework using proximal policy optimization to select informative gene subsets. The reward function balances cluster separation with panel parsimony, and the method handles positive-unlabeled scenarios common in biological data.

Status

Manuscript in preparation. Code will be released with reproducible experiments and ablation studies.