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

Autor: Noman SM; Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China., Zeeshan M; Department of Medicine and Surgery, Al-Nafees Medical College and Hospital, Isra University, Islamabad 44000, Pakistan., Arshad J; Department of Electrical and Computer Engineering, Comsats University Islamabad, Lahore Campus 44000, Lahore, Pakistan., Deressa Amentie M; Department of Information Technology, Assosa University, Assosa 5220, Ethiopia., Shafiq M; Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China., Yuan Y; Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China., Zeng M; Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China., Li X; Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China., Xie Q; Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China., Jiao X; Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China.

Publikováno v: Computational intelligence and neuroscience [Comput Intell Neurosci] 2023 Mar 01; Vol. 2023, pp. 5236168. Date of Electronic Publication: 2023 Mar 01 (Print Publication: 2023).

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.

Autor: Rusic, Doris, Kumric, Marko, Seselja Perisin, Ana, Leskur, Dario, Bukic, Josipa, Modun, Darko, Vilovic, Marino, Vrdoljak, Josip, Martinovic, Dinko, Grahovac, Marko, Bozic, Josko

Publikováno v: Microorganisms; May2024, Vol. 12 Issue 5, p842, 25p

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