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