国际多中心开发集体机器学习驱动主机响应为结核病的基于诊断
Shufa Zheng1,2,3,4, Wenxin Qu1,2,3, Dan Zhang1,2,3
1Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
iScience
|September 25, 2025
概括
诊断活跃肺结核 (TB) 是很困难的. TB-Scope是一种使用宿主基因表达的新型机器学习模型,可以在各种数据集中准确识别活跃的TB.
科学领域:
- * 分子生物学 * 分子生物学
- * 生物信息学是一门学科.
- * 医学中的机器学习
背景情况:
- * 精确的诊断和治疗活动性肺结核病 (TB) 的监测仍然是重大的临床挑战.
- *目前结核病的诊断方法可能很慢,缺乏敏感性,影响患者的治疗结果.
- * 宿主基因表达特征为开发新型诊断工具提供了一个有希望的途径.
研究的目的:
- * 开发和验证基于宿主基因表达的机器学习模型TB-Scope,用于诊断活性肺结核 (ATB).
- * 创建一个强大的诊断决策模型,整合多个分类器,以提高准确性.
- *评估TB-Scope在不同患者群体和数据平台上的表现.
主要方法:
- *使用宿主基因表达数据开发一个整体机器学习分类模型 (TB-Scope).
- *从大型微阵列数据集 (N=1,258) 中选择了143个特征基因 (生物标志物) 用于ATB预测.
- *优化最高分数对 (TSP) 组合分类器和构建一个多分类器决策模型,在12个独立数据集 (N=1,786) 上进行验证.
主要成果:
- *TB-Scope在多个验证数据集中证明了在诊断活性肺结核 (ATB) 中的高准确性.
- *该模型有效地将ATB与健康对照,潜伏结核病感染 (LTBI) 和其他疾病 (ODs) 区分开来.
- *性能在不同国家的不同数据平台 (微阵列和RNA-seq) 和患者人口统计学 (儿童和成人) 上是一致的.
结论:
- * TB-Scope 是一种强大而可靠的工具,用于准确诊断活性肺结核病.
- * 基于宿主基因表达的方法为改善结核病诊断提供了一个有希望的策略.
- * 经过验证的模型显示了在各种全球环境中临床应用的潜力.
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