bvnGPS: a generalizable diagnostic model for acute bacterial and viral infection using integrative host transcriptomics and pretrained neural networks

Li, Qizhi1,2; Zheng, Xubin1,3,4; Xie, Jize1,2; Wang, Ran3; Li, Mengyao1; Wong, Man-Hon3; Leung, Kwong-Sak3,5; Li, Shuai2; Geng, Qingshan1; Cheng, Lixin1

2023-03-01

10.1093/bioinformatics/btad109

BIOINFORMATICS   Impact Factor & Quartile

1367-4803

1367-4811

Motivation The confusion of acute inflammation infected by virus and bacteria or noninfectious inflammation will lead to missing the best therapy occasion resulting in poor prognoses. The diagnostic model based on host gene expression has been widely used to diagnose acute infections, but the clinical usage was hindered by the capability across different samples and cohorts due to the small sample size for signature training and discovery.Results Here, we construct a large-scale dataset integrating multiple host transcriptomic data and analyze it using a sophisticated strategy which removes batch effect and extracts the common information from different cohorts based on the relative expression alteration of gene pairs. We assemble 2680 samples across 16 cohorts and separately build gene pair signature (GPS) for bacterial, viral, and noninfected patients. The three GPSs are further assembled into an antibiotic decision model (bacterial-viral-noninfected GPS, bvnGPS) using multiclass neural networks, which is able to determine whether a patient is bacterial infected, viral infected, or noninfected. bvnGPS can distinguish bacterial infection with area under the receiver operating characteristic curve (AUC) of 0.953 (95% confidence interval, 0.948-0.958) and viral infection with AUC of 0.956 (0.951-0.961) in the test set (N = 760). In the validation set (N = 147), bvnGPS also shows strong performance by attaining an AUC of 0.988 (0.978-0.998) on bacterial-versus-other and an AUC of 0.994 (0.984-1.000) on viral-versus-other. bvnGPS has the potential to be used in clinical practice and the proposed procedure provides insight into data integration, feature selection and multiclass classification for host transcriptomics data.

SCI

English

First ; Corresponding

Shenzhen Science and Technology Program[JCYJ20220530152409020] ; National Key R&D Program of China[2018YFC2001805]

Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics

Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability

WOS:000946431300005

OXFORD UNIV PRESS

BIOLOGY & BIOCHEMISTRY

Web of Science

1.Southern Univ Sci & Technol, Clin Med Coll 2, Shenzhen Peoples Hosp, Affiliated Hosp 1,Jinan Univ, Shenzhen 518020, Peoples R China
2.Shanghai Jiao Tong Univ, John Hopcroft Ctr Comp Sci, Shanghai, Peoples R China
3.Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Hong Kong, Peoples R China
4.Great Bay Univ, Dongguan, Peoples R China
5.Hong Kong Shue Yan Univ, Dept Appl Data Sci, North Point, Hong Kong, Peoples R China

Li, Qizhi,Zheng, Xubin,Xie, Jize,et al. bvnGPS: a generalizable diagnostic model for acute bacterial and viral infection using integrative host transcriptomics and pretrained neural networks[J]. BIOINFORMATICS,2023,39(3).

Li, Qizhi.,Zheng, Xubin.,Xie, Jize.,Wang, Ran.,Li, Mengyao.,...&Cheng, Lixin.(2023).bvnGPS: a generalizable diagnostic model for acute bacterial and viral infection using integrative host transcriptomics and pretrained neural networks.BIOINFORMATICS,39(3).

Li, Qizhi,et al."bvnGPS: a generalizable diagnostic model for acute bacterial and viral infection using integrative host transcriptomics and pretrained neural networks".BIOINFORMATICS 39.3(2023).