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Graphs in Life Sciences

BioData World Webinar
Rik Van Bruggen, Neo4j

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Graphs in Life Sciences

  1. 1. Graphs in Life Sciences Rik Van Bruggen rik@neo4j.com @rvanbruggen
  2. 2. Introduction to Neo4j What We Do
  3. 3. 7/10 20/25 7/10 Adoption Top Retail Firms Top Financial Firms Top Software Vendors Customers Partners Neo4j - The Graph Company The Industry’s Largest Dedicated Investment in Graphs Creator of the Market Leading Neo4j Graph Database Platform ~420 employees HQ in Silicon Valley, other offices include London, Munich, Paris & Malmö ~550 Global Enterprise Customers have launched a Neo4j Trial 76% of the Industry Leaders use Neo4j
  4. 4. The Rise of Connections in Data Networks of People Know s Knows Knows Knows Business Processes Bought Bought Viewed Returned Bought Knowledge Networks Plays Lives_in In_sport Likes Fan_of Plays_for Data connections are increasing as rapidly as data volumes
  5. 5. Harnessing Connections Drives Business Value Enhanced Decision Making Hyper Personalization Massive Data Integration Data Driven Discovery & Innovation Product Recommendations Personalized Health Care Media and Advertising Fraud Prevention Network Analysis Law Enforcement Drug Discovery Intelligence and Crime Detection Product & Process Innovation 360 view of customer Compliance Optimize Operations Connected Data at the Center AI & Machine Learning Price optimization Product Recommendations Resource allocation Digital Transformation Megatrends
  6. 6. Handling Large Graph Work Loads for Enterprises Real-time promotion recommendations Marriott’s Real-time Pricing Engine Handling Package Routing in Real-Time
  7. 7. Improving Analytics, ML & AI for Enterprises Caterpillar’s AI Supply Chain & Maintenance German Center for Diabetes Research (DZD) Financial Fraud Detection & Recovery Top 10 Bank
  8. 8. Neo4j Technology 8
  9. 9. Neo4j is an enterprise-grade native graph database and tools: • Store, reveal and query data relationships • Traverse and analyze any levels of depth in real-time • Add context and connect data to support emerging AI applications Native Graph Technology • • • • • • • •
  10. 10. Labeled Property Graph - Simply Powerful
  11. 11. Graph Databases: Designed for Connected Data TRADITIONAL DATABASES BIG DATA TECHNOLOGY Store and retrieve data Aggregate and filter data Connections in data Real time storage & retrieval Real-Time Connected Insights Long running queries aggregation & filtering “Our Neo4j solution is literally thousands of times faster than the prior MySQL solution, with queries that require 10-100 times less code” Volker Pacher, Senior Developer
  12. 12. Graph databases: Designed for connected data Connections in data Real-Time Connected Insights “Our Neo4j solution is literally thousands of times faster than the prior MySQL solution, with queries that require 10-100 times less code” Volker Pacher, Senior Developer Longer termbenefits: ● Ease of modelling ○ Natural fit for complex domains ○ Business savvy ○ Easy to understand and reason ● Model flexibility in an Agile world ○ Ease of maintenance
  13. 13. Immediate benefits ● Speed, speed, and more speed ○ enabling workloads that were thought to be impossible before ○ real-time what was batch before ● hardware savings: ○ a few servers replacing dozens/hundreds ● operational savings: ○ easier to run and operate Connections in data Real-Time Connected Insights “Our Neo4j solution is literally thousands of times faster than the prior MySQL solution, with queries that require 10-100 times less code” Volker Pacher, Senior Developer Graph databases: Designed for connected data
  14. 14. Real-World Success Stories
  15. 15. Highly Valuable Connected Data Use Cases Drive Enterprise Adoption Network & IT Operations Fraud Detection Identity & Access Management Knowledge Graph Master Data Management Real-Time Recommendations
  16. 16. Graphs in Life Sciences
  17. 17. I will skip the part about ... See https://www.s-cubed-global.com/news/covidgraph-nerds-response-to-the-pandemic
  18. 18. Graphs for Contact Tracing
  19. 19. What do we mean?
  20. 20. Who goes into Isolation/Quarantine?
  21. 21. Different options
  22. 22. How does Contact tracing work?
  23. 23. Centralised or decentralised?
  24. 24. Centralised or Decentralised Centralised: • more privacy sensitive • easier to see anomalies and detect potentially dangerous situations • more valuable to see population-wide patterns, as well as individual health risk benefits • more predictive value Decentralised: - less privacy sensitive - limited views possible wrt population-wide patterns - immediate added value to the individual and their health risks
  25. 25. Graph theory & Contact tracing • Networks and epidemic models
  26. 26. The Model
  27. 27. A synthetic dataset
  28. 28. The Queries
  29. 29. Free for experimentation
  30. 30. Sick person visits same place as healthy person
  31. 31. Overlapping time in place
  32. 32. Most overlap = highest risk
  33. 33. Using inference: MEETS
  34. 34. match path = (p1:Person {healthstatus: "Sick"})-[m:MEETS]->(p2:Person {healthstatus: "Healthy"}) where p1.confirmedtime < m.meetingstarttime return path limit 100
  35. 35. The Analytics
  36. 36. Graph Data Science
  37. 37. Graph data science algorithms
  38. 38. Pagerank: which nodes are likely to be more important
  39. 39. Betweenness: which nodes are likely to contribute to spreading across communities
  40. 40. Community Detection: which parts of the graph "belong" together
  41. 41. Recorded demo - part 1
  42. 42. Recorded demo - part 2
  43. 43. Recorded demo - part 3
  44. 44. Recorded demo - part 4
  45. 45. (synthetic) Contact tracing Demo
  46. 46. Let’s Do Something Amazing Together

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