The emerging role of graph databases in information systems

Lecture for CIS 550

University of Pennsylvania

November 7, 2016

Berger Auditorium

Skirkanich Hall

12:00 pm – 1:20 pm

By Daniel Himmelstein

@dhimmel

Slides at slides.com/dhimmel/cis550

http://www.greenelab.com/

There are many graph databases. I'm most familiar with Neo4j which is:

• an ACID-compliant transactional database with native graph storage and processing
• the most popular graph database according to db-engines.com
• open source:  Community (GPLv3 licensed) & Enterprise (AGPLv3 licensed) Editions

The Graph Mindset

how this class is a microcosm of a larger academic network

What can we do with this graph?

• Course statistics:
  How many students are in CIS 550?
   
• Course recommendations:
  What courses do other students in CIS 550 take?
   
• Course scheduling:
  What room should a course be in so it's nearby other courses that its students are enrolled in?

Source: From Relational to Neo4j

The Relational Database Model

Source: From Relational to Neo4j

The Relational Database Model

Limitations:

1. Relationships require an intermediate table
2. Schemas are cumbersome to create to maintain

Source: From Relational to Neo4j

Relationships inherently form graphs

Source: From Relational to Neo4j

Relational database schema

Source: From Relational to Neo4j

Graph database schema (metagraph)

Emil Eifrem at GraphConnect 2015

The rise of graph databases

Cypher accelerated graph database adoption

From Meet openCypher: The SQL for Graphs. Neo4j Blog

What Cypher looks like

How I became intestested in graphs

http://blog.dhimmel.com/friendship-network/

How do you teach a computer biology?

What's the best software for storing and querying hetnets?

• Hetnet of biology designed for drug repurposing
   
• ~50 thousand nodes
  11 types (labels)
   
• ~2.25 million relationships
  24 types
   
• integrates 29 public resources
  knowledge from millions of studies
   
• the hardest part:
  licensing of publicly available data

Hetionet v1.0

MetaGraph / Data Model / Schema

Visualizing Hetionet v1.0

neo4j.het.io

• Customized Docker image
• Digital Ocean droplet
• SSL from Let's Encrypt
• readonly mode with a query execution timeout
• Custom GRASS style
• Custom guides

Public Hetionet Neo4j Instance

MATCH path =
  // Specify the type of path to match
  (n0:Disease)-[e1:ASSOCIATES_DaG]-(n1:Gene)-[:INTERACTS_GiG]-
  (n2:Gene)-[:PARTICIPATES_GpBP]-(n3:BiologicalProcess)
WHERE
  // Specify the source and target nodes
  n0.name = 'multiple sclerosis' AND
  n3.name = 'retina layer formation'
  // Require GWAS support for the
  // Disease-associates-Gene relationship
  AND 'GWAS Catalog' in e1.sources
  // Require the interacting gene to be
  // upregulated in a relevant tissue
  AND exists(
    (n0)-[:LOCALIZES_DlA]-(:Anatomy)-[:UPREGULATES_AuG]-(n2))
RETURN path

How could multiple sclerosis could affect retina layer formation?

Project Rephetio: drug repurposing predictions

• Hetionet v1.0 contains:
  • 1,538 connected compounds
  • 136 connected diseases
  • 209,168 compound–disease pairs
  • 755 treatments
     
• 1,206 compound–disease metapaths with length ≤ 4
   
• machine learning classifier
   
• predict the probability of treatment for all 209,168 compound–disease pairs (het.io/repurpose)

Project online at thinklab.com/p/rephetio

Project Rephetio: Does bupropion treat nicotine dependence?

• Bupropion was first approved for depression in 1985
   
• In 1997, bupropion was approved for smoking cessation
   
• Can we predict this repurposing from Hetionet? The prediction was:
  • 99.5th percentile for nicotine dependence
  • probability 2.50-fold greater than null

Compound–causes–SideEffect–causes–Compound–treats–Disease

Compound–binds–Gene–binds–Compound–treats–Disease

Compound–binds–Gene–associates–Disease

Compound–binds–Gene–participates–Pathway–participates–Disease

MATCH path = (n0:Compound)-[:BINDS_CbG]-(n1)-[:PARTICIPATES_GpPW]-
  (n2)-[:PARTICIPATES_GpPW]-(n3)-[:ASSOCIATES_DaG]-(n4:Disease)
USING JOIN ON n2
WHERE n0.name = 'Bupropion'
  AND n4.name = 'nicotine dependence'
  AND n1 <> n3
WITH
[
  size((n0)-[:BINDS_CbG]-()),
  size(()-[:BINDS_CbG]-(n1)),
  size((n1)-[:PARTICIPATES_GpPW]-()),
  size(()-[:PARTICIPATES_GpPW]-(n2)),
  size((n2)-[:PARTICIPATES_GpPW]-()),
  size(()-[:PARTICIPATES_GpPW]-(n3)),
  size((n3)-[:ASSOCIATES_DaG]-()),
  size(()-[:ASSOCIATES_DaG]-(n4))
] AS degrees, path
RETURN
  path,
  reduce(pdp = 1.0, d in degrees| pdp * d ^ -0.4) AS path_weight
ORDER BY path_weight DESC
LIMIT 10

Cypher query to find the top CbGbPWaD paths