Game Theory Used to Predict Antimicrobial Resistance

Washington State University researchers are using machine learning and game theory to identify previously unrecognized antibiotic-resistance genes in bacteria. Using this approach, the team was able to determine with 93 to 99 percent accuracy the presence of antibiotic-resistant genes in three different types of bacteria, according to a study published in Scientific Reports today.

In recent years, researchers have been working to make use of genome sequencing to identify antibiotic-resistant genes, looking for similar sequences of genes in public databases. This works for identifying well-known antibiotic-resistant genes, but doesn't hold up with new or unusual genes.

"There appears to be a vast reservoir of antibiotic resistance genes in the natural world," said Douglas Call, one of the paper’s authors. "This tool allows us to identify presumed resistance genes that would not be recognizable based on simple sequence comparisons with public databases."

Using their machine learning algorithm and game theory approach, the researchers looked at the interactions of several features of the genetic material, including its structure and the physiochemical, evolutionary, and composition properties of protein sequences rather than simply its sequence similarity. "This novel game theory approach is especially powerful because features are chosen on the basis of how well they work together as a whole to identify likely antimicrobial-resistance genes—taking into account both the relevance and interdependency of features," said Shira Broschat, another author.

"With the growth in both antimicrobial resistance and the number of available sequenced genomes, using machine learning to predict antimicrobial resistance represents a significant development in providing new and more accurate tools in the field," she added.