Hetionet Awakens: Integrating all of Biology into a Public Neo4j Database

GraphConnect 2016

San Francisco

October 13, 2016

Market Foyer

2:35 pm – 2:50 pm

By Daniel Himmelstein

@dhimmel

Slides at slides.com/dhimmel/graphconnect

    Hetionet is a hetnet — a network with multiple node and relationship types. Version 1.0 contains 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data was integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, perturbations, pharmacologic classes, drug side effects, and disease symptoms.

    Hetionet was created as part of Project Rephetio, an open science project to systematically identify why drugs work and predict new therapies for drugs. Using advanced Cypher queries, we quantified the network connectivity between drug–disease pairs along 1,206 types of paths. We then used machine learning to predict the probability of treatment for 209,168 compound–disease pairs.

    Hetionet is available online as a public Neo4j database instance. The Hetionet Neo4j Browser includes an introductory guide as well as guides showing the most supportive paths for each of the 209,168 predictions. The Hetionet Browser uses Docker for Neo4j. Join us at GraphConnect to learn about how Neo4j is a powerful technology for human disease research.

Presentation Abstract for GraphConnect 2016

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

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

Predictions succeed at prioritizing known treatments

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

See in the Neo4j Browser

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