Using machine learning to predict antimicrobial minimum inhibitory concentrations and associated genomic features for nontyphoidal Salmonella
Olson Robert 1Long S. Wesley 2McDermott Patrick F. 3Davis James J. 1Tyson Gregory H. 3Stevens Rick L. 4Olsen Randall J. 2Nguyen Marcus 1Zhao Shaohua3
作者信息
- 1. University of Chicago Consortium for Advanced Science and Engineering, University of Chicago||Computing, Environment and Life Sciences, Argonne National Laboratory
- 2. Center for Molecular and Translational Human Infectious Diseases Research, Department of Pathology and Genomic Medicine, Houston Methodist Research Institute and Houston Methodist Hospital||Department of Pathology and Laboratory Medicine, Weill Cornell Medical College
- 3. Food and Drug Administration, Center for Veterinary Medicine
- 4. Computing, Environment and Life Sciences, Argonne National Laboratory||University of Chicago, Department of Computer Science
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Key words
Machine learning/Deep learning/Antimicrobial susceptibility testing/Genome sequencing/Diagnostics引用本文复制引用
Olson Robert,Long S. Wesley,McDermott Patrick F.,Davis James J.,Tyson Gregory H.,Stevens Rick L.,Olsen Randall J.,Nguyen Marcus,Zhao Shaohua.Using machine learning to predict antimicrobial minimum inhibitory concentrations and associated genomic features for nontyphoidal Salmonella[EB/OL].(2025-03-28)[2025-12-14].https://www.biorxiv.org/content/10.1101/380782.学科分类
医学研究方法/微生物学/药学
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