New AI methods for predicting protein properties to improve plant nutrient uptake
EU recently presented the From Farm to Fork strategy for fair, healthy, environmentally friendly, and sustainable food production. A main aspect is the reduction of fertilisers in agriculture, a major contributor to pollution.
AI for Life Sciences – Using Deep Semantic Embeddings to Predict Protein Properties to Improve Plant Nutrient Uptake.
By improving plant nutrient uptake, we can reduce polluting nutrients such as nitrogen in fertilisers, reducing nutrient losses and over-fertilisation. Such research is based on laborious, time-consuming, and costly lab experiments.
Increase the probability of engaging in relevant protein regulations.
Our hypothesis is that intrinsic biological property of proteins are encoded in the semantic embeddings that emerge from large-scale deep sequence models applied to protein amino acid sequences. We will exploit these semantic embeddings to predict protein properties using machine learning.
Tommy Löfstedt, Department of Computing Science, Umeå University and Regina Gratz, Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, SLU.