eMIC-AntiKP: Estimating minimum inhibitory concentrations of antibiotics towards Klebsiella pneumoniae using deep
Quang H Nguyen1, Hoang H Ngo1, Thanh-Hoang Nguyen-Vo2
1School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 100000, Viet Nam.
Abstract:
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.
Insights
Antibiotic resistance is a major health concern. This study presents eMIC-AntiKP, a computational tool predicting antibiotic effectiveness against Klebsiella pneumoniae, aiding treatment decisions.
Area of Science:
- Computational biology
- Antimicrobial resistance research
- Genomic data analysis
Background:
- Antibiotic resistance poses a significant threat to patient recovery.
- Accurate antibiotic selection requires timely microbial identification and minimum inhibitory concentration (MIC) determination.
- Conventional methods for MIC determination are often time-consuming.
Purpose of the Study:
- To introduce eMIC-AntiKP, a computational framework for predicting antibiotic MIC values.
- To predict MIC values for 20 antibiotics against Klebsiella pneumoniae.
- To develop a portable and efficient tool for clinical use.
Main Methods:
- Utilized convolutional neural networks (CNNs) for prediction models.
- Employed k-mer counting-based features for model construction.
- Developed an online web server and an offline package for user accessibility.
Main Results:
- The eMIC-AntiKP framework demonstrated satisfactory performance for most antibiotics.
- Prediction accuracy for ampicillin reached 1.00, while cefepime showed 0.49 test 1-tier accuracy.
- Most models achieved test accuracies between 0.70-0.90.
Conclusions:
- eMIC-AntiKP offers an effective and resource-efficient computational approach to predict antibiotic MICs.
- The tool supports efficient antibiotic selection for Klebsiella pneumoniae infections.
- The framework's portability and accessibility enhance its clinical utility.
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