Optimizing Gene-Based Testing for Antibiotic Resistance Prediction
Journal:
arXiv
Published Date:
Feb 19, 2025
Abstract
Antibiotic Resistance (AR) is a critical global health challenge that
necessitates the development of cost-effective, efficient, and accurate
diagnostic tools. Given the genetic basis of AR, techniques such as Polymerase
Chain Reaction (PCR) that target specific resistance genes offer a promising
approach for predictive diagnostics using a limited set of key genes. This
study introduces GenoARM, a novel framework that integrates reinforcement
learning (RL) with transformer-based models to optimize the selection of PCR
gene tests and improve AR predictions, leveraging observed metadata for
improved accuracy. In our evaluation, we developed several high-performing
baselines and compared them using publicly available datasets derived from
real-world bacterial samples representing multiple clinically relevant
pathogens. The results show that all evaluated methods achieve strong and
reliable performance when metadata is not utilized. When metadata is introduced
and the number of selected genes increases, GenoARM demonstrates superior
performance due to its capacity to approximate rewards for unseen and sparse
combinations. Overall, our framework represents a major advancement in
optimizing diagnostic tools for AR in clinical settings.
