发表机构
Arizona State University（亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出LLM在环强化学习框架，用于高维生物信息学特征选择，通过领域建议引导探索和混合奖励提升性能，实验证明其优于基线且收敛更快。
AI 中文摘要
高维生物信息学数据的特点是特征数量相对于样本数量较大，这带来了诸如“维度灾难”等重大挑战，导致过拟合、高计算成本和较差的泛化能力。传统的特征选择方法在此类领域中往往受限于可扩展性和适应性。我们提出了一种用于生物信息学特征选择的LLM在环强化学习（RL）框架，其中RL智能体将特征选择表述为顺序决策任务，而大语言模型（LLM）通过两种方式增强该过程：（1）通过领域知情的建议引导探索，（2）提供整合数据驱动性能与知识驱动评估的混合奖励。LLM还生成解释以提高人类专家的可解释性，而不改变RL策略更新。在多种生物信息学数据集上的实验表明，LLM在环框架优于基线方法，在下游模型中实现稳定性能，并且比纯RL收敛更快。
英文摘要
High-dimensional bioinformatics data, characterized by a large number of features relative to the number of samples, pose major challenges such as the ``curse of dimensionality,'' leading to overfitting, high computational cost, and poor generalization. Traditional feature selection methods often suffer from limited scalability and adaptability in such domains. We propose an LLM-in-the-loop reinforcement learning (RL) framework for bioinformatics feature selection, where the RL agent formulates feature selection as a sequential decision-making task, while the large language model (LLM) enhances the process in two ways: (1) guiding exploration through domain-informed advice, and (2) providing hybrid rewards that integrate data-driven performance with knowledge-driven evaluation. The LLM also produces explanations to improve interpretability for human experts without altering the RL policy update. Experiments on diverse bioinformatics datasets show that the LLM-in-the-loop framework outperforms baselines, achieves stable performance across downstream models, and converges faster than pure RL.
