标题： 基于知识引导的无标签单细胞RNA测序数据生物标志物识别：强化学习视角 显示英文标题

标题： Knowledge-Guided Biomarker Identification for Label-Free Single-Cell RNA-Seq Data: A Reinforcement Learning Perspective

摘要： 基因面板选择旨在识别无标签基因组数据集中最具信息量的基因组生物标志物。 传统方法依赖于领域专业知识、嵌入式机器学习模型或基于启发式的迭代优化，通常会引入偏差和低效性，可能掩盖关键的生物学信号。 为解决这些挑战，我们提出了一种迭代基因面板选择策略，利用现有基因选择算法的集成知识来建立初步边界或先验知识，以指导初始搜索空间。 随后，我们通过由专家行为塑造的奖励函数引入强化学习，实现基因面板的动态优化和针对性选择。 这种整合方式减少了初始边界带来的偏差，同时利用了强化学习的随机适应性。 全面的比较实验、案例研究和下游分析证明了我们方法的有效性，突显了其在无标签生物标志物发现中的改进精度和效率。 我们的结果强调了这种方法在推进单细胞基因组数据分析方面的潜力。 显示英文摘要

摘要： 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.