Clinically Applicable System for Rapidly Predicting Enterococcus faecium Susceptibility to Vancomycin

Author:

Wang Hsin-Yao12ORCID,Chung Chia-Ru3,Chen Chao-Jung45,Lu Ko-Pei6,Tseng Yi-Ju1,Chang Tzu-Hao78,Wu Min-Hsien1891011,Huang Wan-Ting12,Lin Ting-Wei1,Liu Tsui-Ping1,Lee Tzong-Yi1314ORCID,Horng Jorng-Tzong1315,Lu Jang-Jih11617

Affiliation:

1. Department of Information Management, National Central University, Taoyuan City, Taiwan

2. Ph.D. Program in Biomedical Engineering, Chang Gung University, Taoyuan City, Taiwan

3. Department of Computer Science and Information Engineering, National Central University, Taoyuan City, Taiwan

4. Graduate Institute of Integrated Medicine, China Medical University, Taichung, Taiwan

5. Proteomics Core Laboratory, China Medical University Hospital, Taichung, Taiwan

6. Graduate Program in Biomedical Information, Yuan-Ze University, Taoyuan City, Taiwan

7. Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei City, Taiwan

8. Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei City, Taiwan

9. Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan City, Taiwan

10. Division of Haematology/Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan

11. Biosensor Group, Biomedical Engineering Research Center, Chang Gung University, Taoyuan City, Taiwan

12. Department of Pathology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan

13. School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, China

14. Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China

15. Department of Bioinformatics and Medical Engineering, Asia University, Taichung City, Taiwan

16. School of Medicine, Chang Gung University, Taoyuan City, Taiwan

17. Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan City, Taiwan

Abstract

A modified binning method was incorporated to cluster MS shifting ions into a set of representative peaks based on a large-scale MS data set of clinical VRE fm and VSE fm isolates, including 2,795 VRE fm and 2,922 VSE fm isolates. Predictions with the algorithm were significantly more accurate than empirical antibiotic use, the accuracy of which was 0.50, based on the local epidemiology.

Funder

Warshel Institute for Computational Biology

Chang Gung Memorial Hospital, Linkou

Ministry of Science and Technology, Taiwan

National Natural Science Foundation of China

Guangdong Province Basic and Applied Basic Research Fund

Ganghong Young Scholar Development Fund

Futian Project Preliminary Study Fund

Publisher

American Society for Microbiology

Subject

Infectious Diseases,Cell Biology,Microbiology (medical),Genetics,General Immunology and Microbiology,Ecology,Physiology

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