Machine Learning Techniques for Antimicrobial Resistance Prediction of Pseudomonas Aeruginosa from Whole Genome Sequence Data

AIM: Due to the growing availability of genomic datasets, machine learning models have shown impressive diagnostic potential in identifying emerging and reemerging pathogens. This study aims to use machine learning techniques to develop and compare a model for predicting bacterial resistance to a pa...

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Detalles Bibliográficos
Autores principales: Noman, Sohail M., Zeeshan, Muhammad, Arshad, Jehangir, Deressa Amentie, Melkamu, Shafiq, Muhammad, Yuan, Yumeng, Zeng, Mi, Li, Xin, Xie, Qingdong, Jiao, Xiaoyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9995192/
https://www.ncbi.nlm.nih.gov/pubmed/36909968
http://dx.doi.org/10.1155/2023/5236168

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9995192/
https://www.ncbi.nlm.nih.gov/pubmed/36909968
http://dx.doi.org/10.1155/2023/5236168

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