发表机构
Silesian University of Technology; UK Health Security Agency（西里西亚理工大学; 英国卫生安全局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
STUART是一种无比对机器学习框架，利用k-mer特征和特征选择将辐射暴露评估所需的转录组特征降至17个，实现高精度快速分诊，支持便携式测序实时分析。
AI 中文摘要
电离辐射暴露后的快速医学分诊对于应急管理至关重要，然而传统的基于比对的生物信息学方法计算强度过大，难以应对大规模伤亡场景。为解决这一问题，我们开发了STUART（序列分诊与读数转录本量化），一种针对移动生物剂量测定优化的无比对机器学习框架。受自然语言处理（NLP）启发，该系统将原始测序读数转换为基于k-mer的数值特征谱，完全绕过了标准比对流程。虽然该架构普遍适用于任何转录组生物标志物，本研究聚焦于使用FDXR基因模型的辐射暴露场景。在评估逻辑回归、随机森林和XGBoost时，先进的签名选择策略将初始的1024维特征空间大幅缩减了超过98%。在仅保留17个转录组签名的情况下，持续实现了高度稳健的性能——以接近完美的平衡准确率和95-100%范围内的F1分数为特征。关键在于，学习曲线分析表明，完全信号稳定仅需聚合1000条潜在相关读数。此外，在独立数据集上的外部验证产生了超过99%的特异性，证实了所提取签名的组织无关性，尽管细胞来源不同。该框架极低的数据阈值使得实时、边测序边分析的诊断范式得以实现，并与便携式测序仪兼容。通过最小化决策时间，这一去中心化工具弥合了先进生物标志物与实际现场生物监测之间的差距，为快速流行病学响应和常规职业辐射监测提供了可扩展的基础。
英文摘要
Rapid medical triage following ionizing radiation exposure is critical for emergency management, yet traditional alignment-based bioinformatics are too computationally intensive for mass-casualty scenarios. To address this, we developed STUART (Sequence Triage and qUAntification of Read Transcripts), a mapping-free machine learning framework optimized for mobile biological dosimetry. Inspired by Natural Language Processing (NLP), the system converts raw sequencing reads into k-mer-based numerical profiles, completely bypassing standard alignment. While the architecture is universally applicable to any transcriptomic biomarker, this study focused on radiation exposure using the FDXR gene model. Evaluating Logistic Regression, Random Forest, and XGBoost, advanced signature selection strategies drastically reduced the initial 1024-dimensional feature space by over 98%. Highly robust performance - characterized by near-perfect balanced accuracy and F1-scores within the 95-100% range - was consistently achieved while retaining as few as 17 transcriptomic signatures. Crucially, learning curve analysis demonstrated that complete signal stabilization requires aggregating merely 1000 potentially related reads. Furthermore, external validation on an independent dataset yielded over 99% specificity, confirming the tissue-agnostic nature of the extracted signatures despite different cellular origins. The framework's exceptionally low data threshold enables a real-time, analyze-as-you-sequence diagnostic paradigm compatible with portable sequencers. By minimizing time-to-decision, this decentralized tool bridges the gap between advanced biomarkers and practical on-site biomonitoring, offering a scalable foundation for rapid epidemiological response and routine occupational radiation monitoring.
