A machine learning approach to predicting infection rate in the fungal plant pathogen Magnaporthe oryzae
Collaborators: Ryan Ames (Biosciences) and Fabrizio Costa (Computer Science)
IDSAI Research Fellow: Oliver Stoner
Description: Global demand for food is rising and crop production needs to increase in order to provide for a human population expected to be greater than 9 billion by the year 2050. Issues such as water crises, climate change, pests and pathogens can cause significant crop losses and represent major challenges for global food security. In particular, pests and pathogens account for yield losses of up to 20% of the world's harvest each year, with a further 10% loss post-harvest. Our project focusses on fungi, the most important plant pathogens that cause the loss of 125 million tonnes of crops each year. The spread of fungal plant pathogens and the ineffectiveness of available fungicides pose serious threats to global food security.
This project aimed to apply machine learning methods to biological data in order to predict whether fungi will be able to infect different varieties of crops. Being able to accurately predict infection will enable us to forecast food security threats and inform policies that protect crops. Moreover, these methods have the advantage of identifying the key genes, and interactions between genes, that predict infection. These genes therefore, represent potential targets for the next generation of fungicides.
