High quality molecular data
A tiny proportion of plant proteins are experimentally characterised. To address this issue, we've built state-of-the-art machine learning models for functional gene annotation. Simply put, we understand the function of plant genes better than anyone else.
Graphs at our core
Target identification methods such as GWAS and QTL studies cannot prioritise hits. Our platform embraces a graph-centric approach to genomics-modelling, using knowledge graph machine learning to generate meaningful insights about genes in their natural context.
Biology is complex, and failed targets during development provide valuable information for future predictions. Our platform uses advances in Bayesian optimisation and active learning to learn from previous experiments and continuously fine-tune predictions.