  Antibiotic resistance monitoring is of paramount importance in the face of
this on-going global epidemic. Deep learning models trained with traditional
optimization algorithms (e.g. Adam, SGD) provide poor posterior estimates when
tested against out-of-distribution (OoD) antibiotic resistant/non-resistant
genes. In this paper, we introduce a deep learning model trained with
Stochastic Gradient Langevin Dynamics (SGLD) to classify antibiotic resistant
genes. The model provides better uncertainty estimates when tested against OoD
data compared to traditional optimization methods such as Adam.
