Predicting Antibiotic Resistance with a Language AI Model

Abstract

The effectiveness of utilizing a BERT language AI model to predict antibiotic resistance within beta-lactamase/transpeptidase-like proteins is tested. The performance of the model is compared to a traditional Hidden Markov Model (HMM) to verify its capabilities. To further ensure the models capabilities, several tests such as crossvalidation and resistance towards sequence read errors are performed. We find that the BERT model outperforms the HMM model on amino acid sequences with lengths around 40-50 and shorter. These are the type of sequence lengths which we expect to encounter in our use case. For longer sequences, the HMM model is preferable as it requires less computation time. We also find that the BERT model and the HMM model has learned different aspects about the data, allowing the combination of the results of both methods to achieve even finer results. Keywords: