Employing Game Theory to Improve Antimicrobial Resistance Research Studies
In a recent study conducted by researchers at Washington State University, a novel machine learning tool was created using game theory to address the growing public health threat of antimicrobial resistance. As antimicrobial resistance continues to rise in prevalence with over ten million deaths predicted per year globally due to it by the year 2050. By studying antimicrobial resistance, researchers can assess which antibacterial drugs would be best to treat infections. The traditional way of doing this in the past was to culture the bacteria in lab dishes and test various drugs and doses to learn about their impact on bacterial growth, which is time-consuming and unfeasible dependent upon the bacteria.
With the development of whole genomic sequencing, researchers have been able to identify genes that may be antimicrobial resistant using machine learning techniques. In addition to employing advanced computing methodologies, the research study done at Washington State University also uses a feature reduction strategy that improves the accuracy of results by extracting important features from protein sequences. Researchers took into consideration characteristics such as physicochemical, composition, structural, and evolutionary traits in protein sequences. Moreover, the study tested and observed the interdependency of these features to better predict antimicrobial resistance using game theory.
By applying game theory to the tool, the complex biological systems at work can be understood since the interdependency of various features can be observed to a greater extent. This leads to higher accuracy predictions of antimicrobial resistance protein sequences. The methodologies and findings detailed in the article relate directly to the ideas covered in class in regards to game theory and rational decision making. This study is a real-world application of how game theory can be applied in the field of biological sciences and healthcare. Game theory in this study allowed researchers to understand the complex relationships in the system in order to better deliver solutions to address the health threat of antimicrobial resistance. This study allows us to potentially make advancements in predictive medicine by analyzing interdependencies at play.