Machine learning uncovers 'genes of importance' in agri: Study

The researchers conducted experiments that validated eight master transcription factors as genes of importance to nitrogen use

Image credit: Shutterstock

Image credit: Shutterstock

According to a new study published in Nature Communications, machine learning can pinpoint "genes of importance" that help crops to grow with less fertiliser. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture.

Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology. Researchers have been working to determine how to best use the vast amount of genomic data available to predict how organisms respond to changes in nutrition, toxins, and pathogen exposure - which in turn would inform crop improvement, disease prognosis, epidemiology, and public health. However, accurately predicting such complex outcomes in agriculture and medicine from genome-scale information remains a significant challenge.

In the Nature Communications study, NYU researchers and collaborators in the US and Taiwan tackled this challenge using machine learning, a type of artificial intelligence used to detect patterns in data.

As a proof-of-concept, the researchers demonstrated that genes whose responsiveness to nitrogen are evolutionarily conserved between two diverse plant species- Arabidopsis, a small flowering plant widely used as a model organism in plant biology, and varieties of corn, America's largest crop- significantly improved the ability of machine learning models to predict genes of importance for how efficiently plants use nitrogen. Nitrogen is a crucial nutrient for plants and the main component of fertilizer; crops that use nitrogen more efficiently grow better and require less fertilizer, which has economic and environmental benefits.

The researchers conducted experiments that validated eight master transcription factors as genes of importance to nitrogen use efficiency. They showed that altered gene expression in Arabidopsis or corn could increase plant growth in low nitrogen soils, which they tested both in the lab at NYU and in cornfields at the University of Illinois.

Moreover, the researchers proved that this evolutionarily informed machine learning approach can be applied to other traits and species by predicting additional traits in plants, including biomass and yield in both Arabidopsis and corn. They also showed that this approach can predict genes of importance to drought resistance in another staple crop, rice, as well as disease outcomes in animals through studying mouse models.

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