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

Greene Lab

I'm a data scientist

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

Graphs are composed of:

  • Nodes
  • Relationships

Nodes / relationships have type:

  • node labels
    (person, course, university)
  • relationship types
    (lecturer, institution)
  • first_name: Daniel
  • last_name: Himmelstein
  • twitter: @dhimmel
  • SSN: 012-34-5678 
  • catalog: CIS550
  • title: Database & Information Systems
  • units: 5
  • catalog: EPID600
  • title: Data Science for Biomedical Informatics
  • units: 1
  • url: www.upenn.edu
  • founded: 1740
  • type: private
  • league: ivy
  • grade: A-
  • grade: B
  • grade: F

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?

The Relational Database Model

The Relational Database Model

Limitations:

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

Relationships inherently form graphs

Relational database schema

Graph database schema (metagraph)

Emil Eifrem at GraphConnect 2015

The rise of graph databases

Cypher accelerated graph database adoption

What Cypher looks like

How I became intestested in graphs

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

How do you teach a computer biology?

multilayer network, multiplex network, multivariate network, multinetwork, multirelational network, multirelational data, multilayered network, multidimensional network, multislice network, multiplex of interdependent networks, hypernetwork, overlay network, composite network, multilevel network, multiweighted graph, heterogeneous network, multitype network, interconnected networks, interdependent networks, partially interdependent networks, network of networks, coupled networks, interconnecting networks, interacting networks, heterogenous information network

networks with multiple node or relationship types

A 2012 Study identified 26 different names for this type of network:

hetnet

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

dhimmel/hetio
86
5
2
neo4j/neo4j
42,498
3,071
1,007

GitHub stats from 2016-10-09

  • 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

  • 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

Details at doi.org/brsc

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:

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

Mark Kvamme at GraphConnect 2016

Connected data is the future of big data

Questions

Lecture for CIS 550 at Penn: the emerging role of graph databases in information systems

By Daniel Himmelstein

Lecture for CIS 550 at Penn: the emerging role of graph databases in information systems

Lecture on graph databases for Database & Information Systems (CIS 450/550) at the University of Pennsylvania. This course is instructed by Susan Davidson. This presentation is released under a CC BY 4.0 License.

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