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In the story to follow, David presents how his team at Roche was able to identify potential novel targets that were not identified by Open Targets as highly ranked. This was made possible with TypeDB, which his team used to store the relevant data and then find underlying biological evidence for those new targets.
For this purpose, David built out a set of rules and schema in TypeQL. Below is just a very small excerpt of how such data can be modelled — taken from BioGrakn-Covid, a Vaticle community-led project by Konrad Mysliwiec (Data Science Software Engineer, Roche). Note that this is a selected schema; the full schema can be found within the BioGrakn-Covid schema file.