Our agricultural biotech client needed a platform to identify gene targets for desired traits from complex genomic and environmental relationships, based on genome wide association studies (GWAS), and in context of current scientific knowledge.
We used machine learning to identify the association of genomic variants to plant traits. We trained NLP models on a large set of published scientific literature to put variant recommendations in the context of global biomedical knowledge, providing scientists with a better understanding of past studies and the competitive landscape. Early results on crop yields have proven the validity of causal gene recommendations.
