International multicenter development of ensemble machine learning driven host response based diagnosis for tuberculosis
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
Affiliations
Shufa Zheng
Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China; State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
Wenxin Qu
Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China
Dan Zhang
Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China
Jieting Zhou
Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China
Yifan Xu
Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
Wei Wu
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
Chang Liu
Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
Mingzhu Huang
Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
Enhui Shen
Institute of Bioinformatics, Zhejiang University, Hangzhou, China
Xiao Chen
Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China; State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
Michael P. Timko
Departments of Biology and Public Health Sciences, University of Virginia, Charlottesville, VA 22904, USA
Longjiang Fan
Institute of Bioinformatics, Zhejiang University, Hangzhou, China
Fei Yu
Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China; Corresponding author
Dongsheng Han
Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China; Corresponding author
Yifei Shen
Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China; Corresponding author
Summary: 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.
