DOI: 10.25934/pr00010394
title: Genetic basis of AMR using advanced genomic analysis techniques

description: The primary aim of our project is the rapid identification of resistant bacterial strains and a comprehensive understanding of the genetic basis of resistance. By leveraging deep learning algorithms (such as CNNs and RNNs), clustering methods (like K-means and DBSCAN), and feature selection techniques, we intend to analyze the genomic sequences to uncover key genetic markers associated with resistance.
Our research aims to significantly enhance patient outcomes by enabling the rapid identification of antibiotic-resistant bacterial strains. By developing predictive models that pinpoint genetic markers associated with resistance, healthcare providers can swiftly adjust treatment plans, ensuring patients receive the most effective antibiotics. This precision in treatment will reduce the prevalence of ineffective therapies, thereby decreasing mortality rates and improving recovery times.

In terms of strengthening antimicrobial stewardship, our project will empower clinicians with the tools to make informed decisions about antibiotic use. By accurately predicting resistance patterns, we can help prevent the overuse and misuse of antibiotics, which are primary drivers of resistance. This targeted approach will preserve the efficacy of existing antibiotics, slowing the emergence of new resistant strains.

Furthermore, our research will inform public health practices by providing data-driven insights into resistance trends. Public health officials can use this information to develop more effective policies and interventions, ultimately reducing the spread of resistant infections within communities.

Finally, by integrating our findings into health systems, we will strengthen their ability to respond to AMR threats. Our models will be designed for scalability, allowing for broader application in various healthcare settings. This adaptability ensures that even resource-limited environments can benefit from advanced AMR surveillance, leading to more resilient health systems globally.

In summary, our research will drive improvements across patient care, stewardship, public health, and health systems, making a meaningful contribution to the fight against antimicrobial resistance.
Our research aims to significantly enhance patient outcomes by enabling the rapid identification of antibiotic-resistant bacterial strains. By developing predictive models that pinpoint genetic markers associated with resistance, healthcare providers can swiftly adjust treatment plans, ensuring patients receive the most effective antibiotics. This precision in treatment will reduce the prevalence of ineffective therapies, thereby decreasing mortality rates and improving recovery times.

In terms of strengthening antimicrobial stewardship, our project will empower clinicians with the tools to make informed decisions about antibiotic use. By accurately predicting resistance patterns, we can help prevent the overuse and misuse of antibiotics, which are primary drivers of resistance. This targeted approach will preserve the efficacy of existing antibiotics, slowing the emergence of new resistant strains.

Furthermore, our research will inform public health practices by providing data-driven insights into resistance trends. Public health officials can use this information to develop more effective policies and interventions, ultimately reducing the spread of resistant infections within communities.

Finally, by integrating our findings into health systems, we will strengthen their ability to respond to AMR threats. Our models will be designed for scalability, allowing for broader application in various healthcare settings. This adaptability ensures that even resource-limited environments can benefit from advanced AMR surveillance, leading to more resilient health systems globally.