Use of Knowledge Graph for Clinical Multi-Omics Data Integration

The Immunology Drug Discovery Unit of a top-20 pharma company wanted to combine multi-omics datasets from a clinical study with public datasets from the same disease area, and to train a model predicting patient response to treatment, to understand the mechanism of response to its drugs. Clarivate extended an open-source patient-level knowledge-graph architecture, populating it with structured data from MetaBase, Cortellis Drug Discovery Intelligence, ontologies, and the client's own clinical/transcriptomic/proteomic datasets. The resulting central repository reduced information redundancy and infrastructure burden, and its modular graph structure with predefined analytical workflows shortened the time from idea to hypothesis validation, supporting analyses ranging from manual data exploration to graph-AI applications.

The customer is anonymised in Clarivate’s own case study — not withheld by us.

Clarivate owns Cortellis, the curated competitive-intelligence and pipeline-tracking platform standard in pharma portfolio-management teams.

This sits in the Data Providers stage of the AI use-case lifecycle: Named examples from the real-world-data, claims and competitive-intelligence providers pharma AI systems are trained on, benchmarked against, or run inside of.