MS-MLB：一个用于血液基质-MS分类的开源机器学习基准
MS-MLB: An Open Machine Learning Benchmark for Blood-Based MS Classification
AI总结：
该研究提出首个开源MS分类基准MS-MLB，基于GSE17048全血RNA数据，用控制数据泄露的流程评估算法，梯度提升在留存集表现最优，内置外部模型提交路径，仅用于研究比较。
AI中文摘要：
多发性硬化症（MS）通过临床评估、磁共振成像、合适的实验室证据以及排除其他更合理的解释来诊断。血液RNA表达数据可能包含与疾病相关的免疫信号，但血液RNA分类器不能替代临床诊断。本文提出MS-MLB（多发性硬化症机器学习基准），这是一个可复现的开源基准，用于基于机器学习的全血RNA表达数据MS分类研究。MS-MLB使用公开的GSE17048队列，将其转化为MS与健康对照的任务，并在共享的、控制了数据泄露的流程下评估多种算法，研究人员无需重新配置评估即可重新运行该流程。评估内容包括嵌套交叉验证、未触及的分层留存集、自助法置信区间、ROC和精确召回分析、校准测量以及探索性MS研究分数。在最终基准总结中，梯度提升算法在留存集上以93.83的MS研究分数排名第一，对应的AUC-ROC为0.989、灵敏度为0.950、特异性为0.778、F₁分数为0.927、Brier分数为0.050。现有研究已将机器学习应用于MS血液转录组数据，包括PBMC阶段分类和全血诊断特征建模，而本文的贡献不同且更聚焦。据作者所知，MS-MLB是首个专注于从GSE17048全血RNA表达数据进行MS与健康对照分类的开源基准，框架内还内置了有记录的外部模型提交路径。该分数仅用于研究比较，未经过临床验证。该基准可通过此链接访问：this https URL。
英文摘要：
Multiple sclerosis (MS) is diagnosed through clinical assessment, magnetic resonance imaging, laboratory evidence when appropriate, and exclusion of better explanations. Blood RNA expression data may contain disease associated immune signal, but a blood RNA classifier cannot be treated as a replacement for clinical diagnosis. This paper presents MS-MLB (Multiple Sclerosis Machine Learning Benchmark), a reproducible open benchmark for machine learning based MS research classification from whole blood RNA expression data. MS-MLB uses the public GSE17048 cohort, converts it into an MS versus healthy control task, and evaluates multiple algorithms under a shared, leakage controlled pipeline that a researcher can rerun without reconfiguring the evaluation. The evaluation includes nested cross-validation, an untouched stratified holdout set, bootstrap confidence intervals, ROC and precision recall analysis, calibration measurement, and an exploratory MS Research Score. In the final benchmark summary, Gradient Boosting ranked first by MS Research Score on the holdout set, with an MS Research Score of 93.83, AUC-ROC of 0.989, sensitivity of 0.950, specificity of 0.778, $F_{1}$ score of 0.927, and Brier score of 0.050. Prior studies have applied machine learning to MS blood transcriptomic data, including PBMC stage classification and whole blood diagnostic signature modeling. The contribution here is different and narrower. To our knowledge, MS-MLB is the first open benchmark focused on MS versus healthy control classification from GSE17048 whole blood RNA expression data with a documented external model submission pathway built into the framework. The score is intended for research comparison only and has not been clinically validated. The benchmark is accessible here: https://github.com/duckyquang/MS-MLB.
