Antimicrobial Resistance Prediction using Game Theory Algorithm
The increasing prevalence of antibiotic-resistant bacteria is a growing problem around the world. Every year, millions of people are infected with drug-resistant pathogens, and a lot of people die from pneumonia or bloodstream infections.
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.
Washington State University researchers have developed a novel way to identify previously unrecognized antibiotic-resistance genes in bacteria.
In their work, the WSU team decided to use game theory, a tool that is used in several fields, especially economics, to model strategic interactions between game players, to help identify antibiotic resistant genes. In game theory, models determine how the behavior of one participant affects and depends on the behavior of other players.
This novel game theory approach used to determine protein features for this machine learning algorithm has not been used previously for such a purpose and is especially powerful because features are chosen on the basis of how well they work together as a whole to identify putative antimicrobial-resistance genes by taking into account both the relevance and interdependency of features.
By employing machine learning and game theory, the researchers were able to determine with 93 to 99 percent accuracy the presence of antibiotic-resistant genes in three different types of Gram-negative bacteria.