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Identifying Beta-Lactam Resistance with Neural Networks
Conference proceeding

Identifying Beta-Lactam Resistance with Neural Networks

Cory Kromer-Edwards, Jace Neubaum, Suely Oliveira, Caitlin Smith, Evan Walser-Kuntz and Andrew West
2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.1324-1330
11/2019
DOI: 10.1109/BIBM47256.2019.8983058

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Abstract

Antibiotic resistant bacteria are an ever-present threat in today's society and matter of great concern for the medical and scientific communities. With the high frequency of mutations, and the ability to transfer genes across species, bacteria can acquire resistance to currently used antibiotics. Identifying resistance to antibiotics requires growing the bacteria in various antibiotic concentrations to determine the Minimum Inhibitory Concentration (MIC) value for that bacteria-antibiotic combination which is a longer than two day process. Additionally, the testing to evaluate genetic resistance determinants is cumbersome and demands considerable expertise. This paper looks at Neural Networks (NN) and decision trees as viable options to predict the resistance category for an antibiotic, bacteria combination based in the beta-lactamases present in that organism. We show that decision trees have just as high accuracy as NN, and provide more detail and incite in how a decision was made.
 Antibiotics Artificial neural networks decision gene Medical services mic Microorganisms network neural Prediction algorithms Predictive models resistance Roads tree

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