Machine learning has a variety of applications in scientific research, from rapidly analyzing datasets to making predictions. At the Joint BioEnergy Institute (JBEI), researchers are using machine learning to find new proteins that play a role in plant gene expression — providing the scientific community with new avenues to explore in bioenergy crop engineering.
In a new study published in Cell Systems, JBEI researchers used a machine learning algorithm to screen a plant and yeast species to see if they could find proteins involved in gene expression outside of those that have already been identified. In doing so, they confirmed hundreds of proteins that could potentially be new targets for plant engineering research.
“This will help identify new levers to pull to help improve the vision of deploying engineered bioenergy crops to sustain the future bioeconomy,” said corresponding author Patrick Shih, a faculty scientist in the Environmental Genomics and Systems Biology (EGSB) Division and the director of Plant Biosystems Design at JBEI.
