International multicenter development of ensemble machine learning driven host response based diagnosis for tuberculosis.

IF 4.1 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub Date : 2025-08-26 eCollection Date: 2025-09-19 DOI:10.1016/j.isci.2025.113444

Shufa Zheng, Wenxin Qu, Dan Zhang, Jieting Zhou, Yifan Xu, Wei Wu, Chang Liu, Mingzhu Huang, Enhui Shen, Xiao Chen, Michael P Timko, Longjiang Fan, Fei Yu, Dongsheng Han, Yifei Shen

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引用次数: 0

Abstract

Active pulmonary tuberculosis (TB) is challenging to diagnose, and monitoring treatment response effectively remains difficult. To address these challenges, we developed TB-Scope, a host-gene-expression-based ensemble machine learning classification model. Using large-scale microarray datasets (N = 1,258) from three retrospective transcriptomic studies, we selected 143 feature genes (biomarkers) based on their expression ranks to predict ATB. The Top Scoring Pairs (TSP) ensemble classifier for ATB diagnosis was optimized using multi-cohort training samples. We then combined the ATB/Health, ATB/LTBI, and ATB/ODs classifiers to construct an ATB diagnosis decision model (TB-Scope decision). To assess the performance of the TB-Scope algorithm and decision model, we analyzed 12 independent microarray and RNA-seq validation datasets (N = 1,786) comprising both children and adults from seven countries. Thus, our data demonstrates that TB-Scope provides a powerful and reliable tool for accurately diagnosing ATB across diverse data platforms.

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集成机器学习驱动的基于宿主反应的结核病诊断的国际多中心发展。

活动性肺结核的诊断具有挑战性，有效监测治疗反应仍然很困难。为了解决这些挑战，我们开发了TB-Scope，这是一个基于宿主基因表达的集成机器学习分类模型。利用来自三个回顾性转录组学研究的大规模微阵列数据集（N = 1,258），我们根据它们的表达等级选择了143个特征基因（生物标志物）来预测ATB。采用多队列训练样本对ATB诊断的TSP集成分类器进行了优化。然后，我们结合ATB/Health、ATB/LTBI和ATB/ODs分类器构建ATB诊断决策模型（TB-Scope decision）。为了评估TB-Scope算法和决策模型的性能，我们分析了12个独立的微阵列和RNA-seq验证数据集（N = 1,786），包括来自7个国家的儿童和成人。因此，我们的数据表明，TB-Scope为跨不同数据平台准确诊断ATB提供了一个强大而可靠的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

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