The use of knowledge graphs as a data source for machine learning methods to solve complex problems in life sciences has rapidly become popular in recent years. Our Biological Insights Knowledge Graph (BIKG) combines relevant data for drug development from more than 50 public as well as internal data sources to provide insights for a range of tasks: from identifying new targets to repurposing existing drugs. In our case study presentation we are going to discuss our solutions to challenges of building an integrated knowledge graph from diverse sources as well as present two illustrative use case studies exploiting graph data to produce recommendations for domain experts: identifying candidate genes responsible for drug resistance in CRISPR screens and finding promising drugs for repurposing.

Today in this video, Andriy Nikolov is going to introduce a project that they are doing at AstraZeneca which uses knowledge graphs in drug development. Andriy explains the challenges of drug development overall within the space and provides us the motivations to improve on drug development using knowledge graphs through providing data on actionable insights using biological insights knowledge graphs or BIKG. #knowledgegraphs #knowledgegraphconference #knowledgegraphusecasesinhealthcare #knowledgegraphdrugdiscovery