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eMIC-AntiKP: Estimating minimum inhibitory concentrations of antibiotics towards Klebsiella pneumoniae using deep learning
journal contributionposted on 2023-09-12, 16:06 authored by Hoang NguyenHoang Nguyen, HH Ngo, TH Nguyen-Vo, TTT Do, S Rahardja, Binh NguyenBinh Nguyen
Nowadays, antibiotic resistance has become one of the most concerning problems that directly affects the recovery process of patients. For years, numerous efforts have been made to efficiently use antimicrobial drugs with appropriate doses not only to exterminate microbes but also stringently constrain any chances for bacterial evolution. However, choosing proper antibiotics is not a straightforward and time-effective process because well-defined drugs can only be given to patients after determining microbic taxonomy and evaluating minimum inhibitory concentrations (MICs). Besides conventional methods, numerous computer-aided frameworks have been recently developed using computational advances and public data sources of clinical antimicrobial resistance. In this study, we introduce eMIC-AntiKP, a computational framework specifically designed to predict the MIC values of 20 antibiotics towards Klebsiella pneumoniae. Our prediction models were constructed using convolutional neural networks and k-mer counting-based features. The model for cefepime has the most limited performance with a test 1-tier accuracy of 0.49, while the model for ampicillin has the highest performance with a test 1-tier accuracy of 1.00. Most models have satisfactory performance, with test accuracies ranging from about 0.70–0.90. The significance of eMIC-AntiKP is the effective utilization of computing resources to make it a compact and portable tool for most moderately configured computers. We provide users with two options, including an online web server for basic analysis and an offline package for deeper analysis and technical modification.
Preferred citationNguyen, Q. H., Ngo, H. H., Nguyen-Vo, T. H., Do, T. T. T., Rahardja, S. & Nguyen, B. P. (2023). eMIC-AntiKP: Estimating minimum inhibitory concentrations of antibiotics towards Klebsiella pneumoniae using deep learning. Computational and Structural Biotechnology Journal, 21, 751-757. https://doi.org/10.1016/j.csbj.2022.12.041
Journal titleComputational and Structural Biotechnology Journal
Online publication date2022-12-26
Minimum inhibitory concentrationAntimicrobial resistanceAntibioticKlebsiella pneumoniaeConvolutional neural networksk-mer counting46 Information and Computing Sciences4601 Applied ComputingPneumonia & InfluenzaInfectious DiseasesLungPneumonia4903 Numerical and computational mathematics4613 Theory of computation3101 Biochemistry and cell biology