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Pre-Clinical R&D

Pre-Clinical R&D

Case Study

Build an AI platform to find targets for genetic engineering to improve crop yield

Analyze plant genomic data and recommend genetic engineering targets to increase yield and resilience of important, staple food crops using a scalable AI platform


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.

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