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Year, Location, and Species Information In Predicting MIC Values with Beta-Lactamase Genes
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Year, Location, and Species Information In Predicting MIC Values with Beta-Lactamase Genes

Cory Kromer-Edwards, Mariana Castanheira and Suely Oliveira
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.1383-1390
12/16/2020
DOI: 10.1109/BIBM49941.2020.9313331

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Abstract

Antibiotic resistance in bacteria is being recognized as a global health threat. Patients with infections caused by antibiotic resistant bacteria have higher mortality rates and use more hospital resources. The diagnostic of infections caused by antibiotic resistant bacteria is cumbersome and requires bacterial isolation, identification of the species, followed by antibiotic susceptibility testing. This process can take up to 72h. DNA sequencing methods are becoming more convenient and affordable, and the presence of different genes can help predict the susceptibility results of bacteria against antibiotics. We aim to use Machine Learning (ML) to predict the Minimum Inhibitory Concentration (MIC) for bacterial isolates from resistance genes present in their DNA. To do this, we will use information about the presence of beta-lactamase genes identified by whole genome sequencing and its correlation with the susceptibility results (MIC values) for beta-lactam antibiotics. The data was collected worldwide during the years 2016, 2017, and 2018. Species analyzed were Escherichia coli and Klebsiella pneumoniae. All models were trained on different datasets, but all predicted for 2018 data. The training data was split by separator of either year, species, or continent. The species and continent separators had 2016 and 2017 data combined while the year separator had the training datasets of 2016 data, 2017 data, or 2016 and 2017 combined data. It is found that K-Nearest Neighbors performs similarly to Random Forest with ± 12- fold dilution MIC predictions. Random Forests had higher F1-micro scores when predicting the exact MIC value. Training data separated by year outperforms training data separated by continent or species.
 acquisition antibiotic Antibiotics beta-lactamase concentration continent Continents gene Immune system inhibitory k-nearest-neighbors MIC Microorganisms Microwave integrated circuits minimum Prediction algorithms Radio frequency random forest species

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