Maximum values of accuracy for the public dataset when using only resistance genes or when using both resistance and virulence genes.

Maximum values of accuracy for the public dataset when using only resistance genes or when using both resistance and virulence genes.

<p>Maximum values of accuracy for the public dataset when using only resistance genes or when using both resistance and virulence genes.</p>

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Bibliographic Details
Main Author:	Chiara Condorelli (19697665) (author)
Other Authors:	Emanuele Nicitra (19697668) (author), Nicolò Musso (6101027) (author), Dafne Bongiorno (14330361) (author), Stefania Stefani (188822) (author), Lucia Valentina Gambuzza (19697671) (author), Vincenza Carchiolo (17797569) (author), Mattia Frasca (19697674) (author)
Published:	2024
Subjects:	Medicine Microbiology Pharmacology Biotechnology Infectious Diseases Virology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified making infections challenging klebsiella pneumoniae </ div >< p predicting antibiotic resistance drug resistance heightens antimicrobial drugs ineffective predict antimicrobial resistance antimicrobial resistance occurs treat infectious diseases machine learning techniques bacterial genomic data resistance renders antibiotics antimicrobial resistance machine learning genomic data support clinicians severe illness pathogen genomes medications employed longer respond genome composition gene content even thousands disease spread containing hundreds
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Summary:	<p>Maximum values of accuracy for the public dataset when using only resistance genes or when using both resistance and virulence genes.</p>

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