Prediction of Acquired Antimicrobial Resistance for Multiple Bacterial Species Using Neural Networks
Derya Aytan-Aktug, Philip T. L. C. Clausen, Valeria Bortolaia, Frank M. Aarestrup, Ole Lund
Abstract
Machine learning is a proven method to predict AMR; however, the performance of any machine learning model depends on the quality of the input data. Therefore, we evaluated different methods of representing information about mutations as well as mobilizable genes, so that the information can serve as input for a robust model. We combined data from multiple bacterial species in order to develop species-independent machine learning models that can predict resistance profiles for multiple antimicrobials and species with high performance.
Topics & Concepts
Antibiotic resistanceArtificial neural networkAntimicrobialResistance (ecology)BiologyMicrobiologyArtificial intelligenceComputational biologyComputer scienceEcologyAntibioticsAntibiotic Use and ResistanceAdvanced Chemical Sensor TechnologiesBacterial Identification and Susceptibility Testing