Abstract:
Antimicrobial resistance (AMR) presents an immediate threat to public health as microorganismsevolve to resist antimicrobial drugs, leading to challenging or untreatable infections.AMR-related costs could exceed 1 trillion dollars globally by 2050, surpassing major causesof death. In 2019, AMR directly caused 1.27 million deaths worldwide, surpassing mortalityrates of HIV/AIDS and malaria. In particular, tuberculosis (TB) claimed 1.3 million livesin 2022, ranking as the second most prevalent infectious disease globally. This study introducesin silico approach utilizing deep learning to analyze the entire genome sequence of toppathogens such as Escherichia coli, Staphylococcus aureus, Klebsiella pneumonia, Streptococcus pneumoniae, Pseudomonas aeruginosa, and Mycobacterium tuberculosis. The goalis to swiftly and accurately predict a pathogen’s resistance to specific drugs, eliminating thenecessity for complex laboratory experiments. In addition to employing the standard labelencoding technique, this study introduces three novel encoding methods for mutationaldata: transition-transversion encoding, codon frequency encoding, and gene based codongain. These novel techniques are the major contribution of this study. Prediction of the AMR profile of the patient’s pathogen against various drugs is crucial before prescribing treatment.This approach helps in identifying the most appropriate drug that will be effective in treatingthe patient at the early stage, thereby reducing the likelihood of treatment failure causedby prescribing a drug to which the pathogen is resistant. While the model’s performance varies across different drugs and pathogens, it demonstrates the potential for application inantimicrobial resistance prediction.
