An Automated Machine Learning Framework for Antimicrobial Resistance Prediction Through Transcriptomics

Abstract

The emergence of antimicrobial resistance (AMR) poses a global threat of growing concern to the healthcare system. To mitigate the spread of resistant pathogens, physicians must identify the susceptibility profile of every patients infection in order to prescribe the appropriate antibiotic. Furthermore, disease control centers need to be able to accurately track the patterns of resistance and susceptibility of pathogens to different antibiotics. To achieve this, high-throughput methods are required to accurately predict the resistance profile of a pathogenic microbe in an automated manner. In this work, a transcriptomics-based approach utilizing a machine learning framework is used to achieve this goal. The study highlights the potential of using gene expression as an indicator of resistance to different antibiotics. Results indicate the importance of starting with a high-quality training dataset containing high genetic diversity and a sufficient number of resistant samples. Furthermore, the performed analysis reveals the importance of developing new methods of feature reduction specific to transcriptomic data. Most importantly, this study serves as a proof-of-concept to the potential impact of deploying such models to reduce the mortality rate associated with AMR.