Videos

Knowledge graphs are key to extracting insight from volumes of available biomedical data. In this session, we’ll cover a practical example of how Neo4j + Graph Data Science can be used to create graph-based features to help identify potential gene targets for a given disease. Crossr: https://www.crossr.co.uk/ Blog: https://www.crossr.co.uk/post/recommendation-systems-in-drug-discovery Ben Vozza: https://www.linkedin.com/in/ben-vozza-a652b695/ Lightning Talk @ NODES 2022: https://youtu.be/q5061JL5LaU All biology is computational biology: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2002050 Hetionet: https://het.io/ Open Targets: https://platform.opentargets.org/ Clinical Knowledge Graph: https://ckg.readthedocs.io/en/latest/INTRO.html A systems-level framework for drug discovery identifies Csf1R as an anti-epileptic drug target https://pubmed.ncbi.nlm.nih.gov/30177815/ StringDB: StringDB: https://string-db.org/ Doctor.AI Live Stream: https://youtube.com/live/lZidMw6vVWY 0:00 Intro 3:40 Crossr Overview 7:05 Open Data in Life Sciences 13:00 Biomedical Knowledge Graph – Crossr Demo 19:05 FDA Modernization Act 2.0 24:10 Crossr Demo (contd) 44:10 Q&A & Contact 1:01:00 WrapUp #neo4j #lifescience #healthcare #biomedical #knowledgegraph