Neo4j for Healthcare & Life Sciences
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The document discusses the application of Neo4j in healthcare and life sciences, highlighting its capabilities in managing graph data, patient information security, and compliance. It covers the importance of graph data science in understanding relationships for predictions, with an emphasis on tools and methods for data integration and analysis. Additionally, the document includes examples of how Neo4j can be used for clinical trials, drug discovery, and patient journey modeling.
Neo4j for Healthcare & Life Sciences
- 1. Neo4j for Healthcare & Life Sciences Pat Wall- Director of Product Marketing Alicia Frame, PhD - Lead Product Manager, Data Science
- 2. 2 Agenda 1. Intro to Neo4j for Life Sciences 2. Graph Data Science Overview 3. Demonstration
- 3. 3 Neo4j for Life Sciences
- 5. Allow organizations to support multiple graphs in their Neo4j footprint 5 Allow organizations to store more sensitive data in their graphs Neo4j 4.1 Allow organizations to scale their Neo4j infrastructure
- 7. Competitive Intelligence Compounds Research Compounds Data Sources: ● Worksheets ● PubMed ● CT.gov ● Google Patents ● FDA ● Academic Papers ● Grant Applications Research Compounds Research Use r Compounds Workb ooksClinical Trials Patents Research Workb ooks Workb ooks Workb ooks FDA
- 11. 11 Exponential Data Growth Connections Value Workbooks Compounds Research Clinical Trials 10 M 100 M 1 B 5 B
- 12. 12 Exponential Data Growth Connections Value Workbooks Compounds Research Clinical Trials 10 M 100 M 1 B 5 B 10B + EMR Real World Evidence
- 14. Who can access the medical information for patients during a clinical trial? How do I set up permissions so the right individual can see the correct data? ● Read vs. Write Access in Graphs Do I want my administrators to have access to data? 14 Patient Information Security and Compliance
- 16. Competitive Intelligence Compounds Research Compounds Research Compounds Research Use r Compounds Workb ooksClinical Trials Patents Research Workb ooks Workb ooks Workb ooks FDA Clinical Trial Team Regulatory & Compliance TeamClinical Trial Team
- 17. Competitive Intelligence Compounds Research Compounds Research Compounds Research Use r Compounds Workb ooksClinical Trials Patents Research Workb ooks Workb ooks Workb ooks FDA Clinical Trial Team Regulatory & Compliance TeamClinical Trial Team Everyone
- 18. 18 Native Graph Technology Analytics Tooling Graph Transactions Data Integration Dev. & Admin Drivers & APIs Discovery & Visualization Graph Analytics
- 19. 19 Perspective Search Visualize Explore Inspect Edit Explore & Collaborate with Neo4j Bloom Explore Graphs Visually Prototype Concepts Faster Collaborate Across Teams
- 20. 20
- 22. for Graph Data ScienceTM Neo4j Graph Data Science Library Scalable Graph Algorithms & Analytics Workspace Native Graph Creation & Persistence Neo4j Database Visual Graph Exploration & Prototyping Neo4j Bloom Practical Integrated Intuitive
- 23. Graph Data Science is a science-driven approach to gain knowledge from the relationships and structures in data, typically to power predictions. 23 What is Graph data science? Data scientists use relationships to answer questions.
- 24. Query (Cypher) Real-time, local decisioning and pattern matching Graph Algorithms Global analysis and iterations You know what you’re looking for and making a decision You’re learning the overall structure of a network, updating data, and predicting Local Patterns Global Computation
- 25. Robust Graph Algorithms • Run on a loaded graph to compute metrics about the topology and connectivity • Highly parallelized and scale to 10’s of billions of nodes 25 The Neo4j GDS Library Mutable In-Memory Workspace Computational Graph Native Graph Store Efficient & Flexible Analytics Workspace • Automatically reshapes transactional graphs into an in-memory analytics graph • Optimized for analytics with global traversals and aggregation • Create workflows and layer algorithms
- 26. Evolution of Graph Data Science Decision Support Graph Based Predictions Graph Native Learning 26 Graph Feature Engineering Graph Embeddings Graph Networks Knowledge Graphs Graph Analytics
- 27. Evolution of Graph Data Science Graph Feature Engineering Graph Embeddings Graph Networks 27 Graph AnalyticsKnowledge Graphs Graph search and queries Support domain experts
- 28. Deceptively Simple Queries What completes the connections from genes to diseases to targets? What genes can be reached +4 hops out from a known drug target? What mechanisms in common are there between two drugs? Which biological pathways are regulated by these genes? Knowledge Graph Queries in Drug Discovery Connecting concepts like genes, chemicals, and diseases lets you link and mine interconnected concepts like genes, diseases, and chemicals. 28
- 29. Evolution of Graph Data Science Graph Feature Engineering Graph Embeddings Graph Networks 29 Knowledge Graphs Graph Analytics Graph queries & algorithms for offline analysis Understanding Structures
- 30. Graph Algorithms for Drug Discovery PageRank & Betweenness to identify essential regulatory genes or drug targets Louvain to identify protein regulatory networks Shortest path to link drug targets to possible outcomes or side effects Node Similarity to find structurally similar chemicals Link Prediction to estimate likelihood of interactions Identity drug mechanisms and new targets based on network structure
- 31. Evolution of Graph Data Science Graph Embeddings Graph Networks 31 Knowledge Graphs Graph Analytics Graph Feature Engineering Graph algorithms & queries for machine learning Improve Prediction Accuracy
- 32. Graph Feature Engineering in Drug Discovery Feature Engineering is how we combine and process the data to create new, more meaningful features. Using graphs we can base ML on genes with similar network topology. 32 Gene and Target Data
- 33. Evolution of Graph Data Science Decision Support Graph Based Predictions Graph Native Learning 33 Graph Feature Engineering Graph Embeddings Graph Networks Knowledge Graphs Graph Analytics FUTUREEarly Adopters
- 35. The Data 35 Synthetic electronic medical records from - Open source, synthetic patient generator developed by the MITRE corporation - Flexibly generates medical history of synthetic patients - Intended to be realistic but not real - data distribution and correlations mimic real EHRs code & data are available at github.com/AliciaFrame/GDS_patient_journey_demo
- 36. Thank You & Questions 36 - O’Reilly Book on Graph Algorithms neo4j.com/graph-algorithms-book/ - GDS website, whitepapers, links neo4j.com/use-cases/graph-data-s cience-artificial-intelligence/ - Patient Journey Demo: github.com/AliciaFrame/GDS_Patie nt_Journey - GDS Sandbox sandbox.neo4j.com/ ?usecase=graph-data-science Resources Pat Wall [email protected] Alicia Frame [email protected] @AliciaFrame1