Machine learning for digItal diagnostics of antimicrobial resistance

Project Details

Description

The discovery of antimicrobial agents was one of the great triumphs of the 20th century. The sobering news is that antimicrobial resistance (AMR) was part of the process as well. Both the financial cost and the cost in public health is expected to be huge in coming decades, unless better methods to tackle AMR are found. In the MAGITICS project, new machine learning approaches will be developed and applied for modelling AMR for faster diagnosis, better surveillance and prediction of resistance emergence. To achieve this, we have assembled a transnational team (Canada, China, Finland, France) with complementary skills in machine learning, bioinformatics as well as AMR expertise. The aim of the project is to deliver a framework to address AMR using state-of-the-art tools and databases of high quality for the benefit of the research community as well as public health agencies.
Short titleMAGITICS/Rousu
AcronymMAGITICS
StatusActive
Effective start/end date01/01/202031/12/2023

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.