Case Study: Queen’s University accelerates cancer research with Ontotext’s Target Discovery Platform

Research Lab at Queen’s University in Canada Accelerates Cancer Research with AI

Queen’s University’s research lab in Canada needed to speed up the validation of genes related to cancer research, but manual screening across genomic databases, pathways, interactions, and literature created a backlog of more than 2,000 targets and could take months. To address this, the team used Ontotext’s Target Discovery Platform to build stronger evidence for shortlisting candidates and streamline their prioritization process.

Ontotext’s Target Discovery unified data from hundreds of datasets and millions of scientific sources into a knowledge graph, automatically extracting and contextualizing facts to support each candidate with clear evidence and provenance. As a result, Queen’s University reduced research time from months to days, discovered insights 500% faster, improved confidence in shortlisted genes, and lowered the cost and risk of downstream lab experiments.

Queen’s University