Tackling Antimicrobial Resistance with Artificial Intelligence

Researchers from the Centre for Antimicrobial Optimisation (CAMO) and the Centre for Bio-Inspired Technology at Imperial College London (ICL) have developed a new data-driven method to increase multiplexing capabilities of widely used PCR instrumentation.

In two studies published last month, the team at ICL, demonstrated the method using single-molecule real-time PCR. This increases the throughput of molecular diagnostic platforms and reduces the cost of tests, without any changes to instrument hardware, by virtue of smarter data analytics.


Artificial Intelligence DNA

“There is plenty of room to maximise the value of existing data using sophisticated machine learning methods.”

-Dr Jesus Rodriguez-Manzano

CAMO Chief Scientist


In the first study, the team explored ways to enhance multiplexing capabilities by training machine learning models using the kinetic information in DNA/RNA amplification curves. As a proof-of-concept study, this was shown using a 3-plex assay targeting common carbapenemase genes (KPC, NDM and VIM). In the second study, the group incorporated thermodynamic information via melting curves, to enhance the method to high-level multiplexing applications, such as detecting nine variants of mobilised colistin resistance (mcr-1 to mcr-9).

For more information, read media coverage of the new methods from GenomeWeb.

 

written by:

Jesus Rodriguez-Manzano

Leave a Reply

Your email address will not be published.