BioData World Webinar Rik Van Bruggen, Neo4j

1. Graphs in Life Sciences
Rik Van Bruggen
rik@neo4j.com
@rvanbruggen

2. Introduction to Neo4j
What We Do

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. 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. 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. Handling Large Graph Work Loads for Enterprises
Real-time promotion
recommendations
Marriott’s Real-time
Pricing Engine
Handling Package
Routing in Real-Time

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. Neo4j Technology
8

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. Labeled Property Graph - Simply Powerful

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. 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. 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. Real-World
Success Stories

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. Graphs
in
Life Sciences

17. I will skip the part about ...
See https://www.s-cubed-global.com/news/covidgraph-nerds-response-to-the-pandemic

18. Graphs for
Contact Tracing

19. What do we mean?

20. Who goes into Isolation/Quarantine?

21. Different options

22. How does Contact tracing work?

23. Centralised or decentralised?

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. Graph theory & Contact tracing
•
Networks and epidemic
models

26. The Model

27. A synthetic dataset

28. The Queries

29. Free for experimentation

30. Sick person visits same place as healthy person

31. Overlapping time in place

32. Most overlap = highest risk

33. Using inference: MEETS

34. match path = (p1:Person {healthstatus: "Sick"})-[m:MEETS]->(p2:Person {healthstatus: "Healthy"})
where p1.confirmedtime < m.meetingstarttime
return path
limit 100

35. The Analytics

36. Graph Data Science

37. Graph data science algorithms

38. Pagerank:
which nodes are likely to be
more important

39. Betweenness:
which nodes are likely to
contribute to spreading
across communities

40. Community Detection:
which parts of the graph
"belong" together

42. Recorded demo - part 1

43. Recorded demo - part 2

44. Recorded demo - part 3

45. Recorded demo - part 4

46. (synthetic)
Contact tracing
Demo

47. Let’s Do Something
Amazing Together