中文版 | English
Title

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

Author
Corresponding AuthorGeng, Qingshan; Cheng, Lixin
Publication Years
2023-03-01
DOI
Source Title
ISSN
1367-4803
EISSN
1367-4811
Volume39Issue:3
Abstract
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.
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
First ; Corresponding
Funding Project
Shenzhen Science and Technology Program[JCYJ20220530152409020] ; National Key R&D Program of China[2018YFC2001805]
WOS Research Area
Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS Subject
Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS Accession No
WOS:000946431300005
Publisher
ESI Research Field
BIOLOGY & BIOCHEMISTRY
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/513414
DepartmentShenzhen People's Hospital
Affiliation
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
First Author AffilicationShenzhen People's Hospital
Corresponding Author AffilicationShenzhen People's Hospital
First Author's First AffilicationShenzhen People's Hospital
Recommended Citation
GB/T 7714
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).
APA
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).
MLA
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).
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