Paper ID: 2501.04718 • Published Jan 2, 2025
Knowledge-Guided Biomarker Identification for Label-Free Single-Cell RNA-Seq Data: A Reinforcement Learning Perspective
Meng Xiao, Weiliang Zhang, Xiaohan Huang, Hengshu Zhu, Min Wu, Xiaoli Li, Yuanchun Zhou
TL;DR
Get AI-generated summaries with premium
Get AI-generated summaries with premium
Gene panel selection aims to identify the most informative genomic biomarkers
in label-free genomic datasets. Traditional approaches, which rely on domain
expertise, embedded machine learning models, or heuristic-based iterative
optimization, often introduce biases and inefficiencies, potentially obscuring
critical biological signals. To address these challenges, we present an
iterative gene panel selection strategy that harnesses ensemble knowledge from
existing gene selection algorithms to establish preliminary boundaries or prior
knowledge, which guide the initial search space. Subsequently, we incorporate
reinforcement learning through a reward function shaped by expert behavior,
enabling dynamic refinement and targeted selection of gene panels. This
integration mitigates biases stemming from initial boundaries while
capitalizing on RL's stochastic adaptability. Comprehensive comparative
experiments, case studies, and downstream analyses demonstrate the
effectiveness of our method, highlighting its improved precision and efficiency
for label-free biomarker discovery. Our results underscore the potential of
this approach to advance single-cell genomics data analysis.