In this session, we'll present in silico drug repurposing through machine learning analysis of a biochemical knowledge graph. Drug repurposing is the process that identifies the use of an existing drug to a novel protein target. This procedure can save a lot of time and funds during drug discovery and development phases. The session will demonstrate the integration of heterogenous chemical, biological, and clinical data in a biochemical knowledge graph. The application of machine learning aims to perform link prediction in the graph and identify novel targets for existing drugs. An end-to-end workflow, combining both theoretical and practical parts, will be presented. The key concepts will be introduced, while samples of code and queries will follow each step of the pipeline.
Speakers: Sotiris Ouzounis, Alexandros Kanterakis, Vasilis Panagiotopoulos
Format: Full Session 30-45 min
Level: Advanced
Topics: #graphdatascience , #machinelearning , #knowledgegraph , #pharmaceutical , #healthcare , #advanced
Region: APAC
Slides: https://dist.neo4j.com/nodes-20202-slides/053%20CloudScreen%20A%20Graph-Based%20Drug%20Repurposing%20Platform%20Empowered%20by%20Machine%20Learning%20-%20NODES2022%20APAC%20Advanced%206%20-%20Sotiris%20Ouzounis%2C%20Alexandros%20Kanterakis%2C%20Vasilis%20Panagiotopoulos.pptx
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