Author(s) :

Bindhushree.

Volume/Issue :

Abstract :

Antimicrobial resistance (AMR) is rapidly becoming one of the most significant challenges in modern medicine, complicating the treatment of bacterial infections. As pathogens evolve resistance to multiple classes of antibiotics, early detection and prediction of AMR have become critical. Traditional methods for identifying antimicrobial resistance are often slow and labor-intensive. However, deep learning (DL) methods, particularly those that analyze genomic data, have shown great promise in improving the speed and accuracy of AMR predictions. By leveraging bacterial genomic sequences, deep learning models can predict resistance patterns and provide insights into the genetic underpinnings of AMR. This article explores the application of deep learning techniques in the prediction of AMR from bacterial genomes, emphasizing the advantages and challenges of these methods. It also highlights the potential future directions for integrating deep learning with genomic technologies to improve the detection, management, and treatment of antimicrobial-resistant infections.

No. of Downloads :

0