发表机构
University of Nebraska-Lincoln; University of Nebraska Medical Center（内布拉斯加大学林肯分校; 内布拉斯加大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MarkerScout是一种疾病无关的机器学习框架，在三种传染病的6个队列中表现出良好性能，其生物标志物特征在独立临床数据集基准测试中优于多数随机特征集。
AI 中文摘要
我们在来自配套机制免疫模拟平台的三种传染病上演示该框架：SARS-CoV-2、甲型流感病毒（Influenza A Virus）和恶性疟原虫（Plasmodium falciparum）。每种疾病均针对住院患者和重症监护室（ICU）队列进行评估，共产生6个队列。最佳管道的交叉验证宏F1值在甲型流感病毒-住院队列（IAV-HOSP）中为0.82，在新冠病毒-ICU队列（COV-ICU）中为0.99，且该框架为每种疾病和阶段生成了分层的、具有方向感知的生物标志物列表。白细胞介素-18（IL-18）在新冠病毒的两个阶段均达到最强分层且方向一致。当与三个独立收集的临床ICU数据集进行基准测试时，MarkerScout排名靠前的特征在新冠病毒上优于94.4%的同等规模随机选择的特征集，在甲型流感病毒上优势较弱但方向一致（66.7%），在恶性疟原虫上为60.7%。
英文摘要
We demonstrate the framework on three infectious diseases derived from a companion mechanistic immune-simulation platform: SARS-CoV-2, Influenza A Virus, and Plasmodium falciparum. Each disease was evaluated across hospitalization and intensive care unit cohorts, yielding six cohorts in total. Best-pipeline cross-validated macro F1 ranged from 0.82 for IAV-HOSP to 0.99 for COV-ICU, and the framework produced tiered, direction-aware biomarker lists for each disease and phase. Interleukin-18 (IL-18) reached the strongest tier in both SARS-CoV-2 phases with consistent direction. When benchmarked against three separate, independently collected clinical ICU datasets, MarkerScout's top-ranked features outperformed 94.4% of randomly selected feature sets of equivalent size for SARS-CoV-2, with a weaker but directionally consistent advantage for Influenza A Virus (66.7%) and Plasmodium falciparum (60.7%).
Comments6 figures, 36 pages
