用强化学习优化抗生素耐药基因检测，提升诊断准确性。
Optimizing Gene-Based Testing for Antibiotic Resistance Prediction
- 结合强化学习与变压器模型，智能筛选关键耐药基因。
- 引入元数据后，基因数量增加时表现优于现有方法。
- 适合临床诊断场景，尤其在基因组合稀疏时仍稳定可靠。
抗生素耐药性（AR）是全球重大健康挑战，亟需开发成本低、高效且准确的诊断工具。鉴于耐药性的遗传基础，针对特定耐药基因的聚合酶链反应（PCR）技术成为预测诊断的可行方案。本文提出GenoARM框架，融合强化学习（RL）与基于Transformer的模型，优化PCR基因检测的选择，利用观测到的元数据提升预测精度。我们在多个公开的真实细菌样本数据集上构建了高性能基线模型进行对比。结果表明，未使用元数据时，所有方法均表现良好且可靠；引入元数据并增加基因数量后，由于能有效近似未见和稀疏组合的奖励，GenoARM展现出显著优势。该框架为临床环境中优化抗生素耐药性诊断工具提供了重要进展。
原文摘要 · Abstract (English)
Antibiotic Resistance (AR) is a critical global health challenge that necessitates the development of cost-effective, efficient, and accurate diagnostic tools. Given the genetic basis of AR, techniques such as Polymerase Chain Reaction (PCR) that target specific resistance genes offer a promising approach for predictive diagnostics using a limited set of key genes. This study introduces GenoARM, a novel framework that integrates reinforcement learning (RL) with transformer-based models to optimize the selection of PCR gene tests and improve AR predictions, leveraging observed metadata for improved accuracy. In our evaluation, we developed several high-performing baselines and compared them using publicly available datasets derived from real-world bacterial samples representing multiple clinically relevant pathogens. The results show that all evaluated methods achieve strong and reliable performance when metadata is not utilized. When metadata is introduced and the number of selected genes increases, GenoARM demonstrates superior performance due to its capacity to approximate rewards for unseen and sparse combinations. Overall, our framework represents a major advancement in optimizing diagnostic tools for AR in clinical settings.
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