Data Driven Decisions in Life Sciences powered by Knowledge Graphs. Example on Clinical Trials information
In The last decade, Data has become the new oil, and as a key asset in all industries. However, to leverage the power of data, it needs to be refined and distributed. This transformation has largely affected the Life Science field from academy to industry, who has adhered largely to the FAIR principles of findability, accessibility, interoperability, and reusability. Data is not consumables from an experiment anymore, it is now set to be re-used, re-interpreted… Data becomes more re-usable however it is necessary to consolidate views of the disparate and siloed data, as well setting the infrastructure to search, retrieve and analyse it. The use of a Semantic Web framework and Knowledge graphs presents great benefits to integrate, harmonize scientific knowledge, and provide ideal support for research to search, explore hypotheses and refine insights. Knowledge graphs have large application along drug development: from target identification, clinical trial design, drug repurposing or real-world evidence data. It allows not only to provide a good reasoning framework for users, but also for advanced analytics, supporting data-driven decisions and search-refine efficiency. The DisQover knowledge platform integrates data silos into a knowledge graph, which is used for explorative search. Life Sciences concepts are anchored to canonical entities, and the relationships between concepts are used to navigate on the graph. For example, public clinical registries, internal clinical trial management systems and licensed data are integrated together on the knowledge platform, keeping the semantic structure with an abstracted model for user navigability. Such integration supports for example clinical competitive analysis or clinical trial design, and could be extended progressively to new data assets, broadening the field of applications.
