The hetnet awakens in Philadelphia.

biology ⭃ network

DataPhilly Meetup

Papadakis Building, Room 120

June 6, 2017

Slides at slides.com/dhimmel/dataphilly

http://www.greenelab.com/

DataPhilly Talk Abstract:

Hetnets are networks with multiple node and relationship types. He will discuss when hetnets are the right tool for integrating and analyzing diverse types of data. Specifically, he'll showcase Project Rephetio, which predicts new uses for existing drugs. This project created Hetionet, a network with 2.25 million relationships of 24 types, allowing researchers to ask questions that span the many realms of biomedical knowledge.

About Daniel Himmelstein:

Daniel Himmelstein is a "digital craftsman of the biodata revolution" who works in the Greene Lab at Penn. In 2016, he received his PhD in Biological & Medical Informatics from UCSF. Daniel leads the Cognoma (DataPhilly) Datathon and was a finalist for "Scientist of the Year" in the 2016 Philly Geek Awards. His research focuses on integrating open data to uncover the secrets of human health.

https://www.meetup.com/DataPhilly/events/240213100/

How I became intestested in graphs

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

My Facebook friendship network in 2014

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

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

Visualizing Hetionet v1.0

• Hetnet of biology for drug repurposing
   
• ~50 thousand nodes
  11 types (labels)
   
• ~2.25 million relationships
  24 types
   
• integrates 29 public resources
  knowledge from millions of studies

Hetionet v1.0

• Nodes
  • standardized vocabularies
  • stable, unambiguous identifiers
     
• Relationships:
  • Omics scale required
  • Literature mining
  • High throughput experimental technologies
  • Avoid manual mapping
     
• Versioned data dependencies with GitHub commit hash URLs
  pandas-dev/pandas#14576

Constructing 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?

More queries at thinklab.com/d/220

Project Rephetio contributions on Thinklab

(see thinklab.com/p/rephetio/leaderboard)

Project Rephetio: drug repurposing predictions

• Hetionet v1.0 contains:
  • 1,538 connected compounds
  • 136 connected diseases
  • 209,168 compound–disease pairs
  • 755 treatments
• Systematic drug repurposing:
  • Compare the therapeutic utility of data types
  • Identify the mechanisms of drug efficacy
  • Predict the probability of treatment for all 209,168 compound–disease pairs (het.io/repurpose)
• Project online at thinklab.com/p/rephetio

Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Daniel S Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E Baranzini
bioRxiv. 2016. DOI: 10.1101/087619

Machine learning methodology

Predictions succeed at prioritizing known treatments

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

Browse all predictions at het.io/repurpose. Discuss at thinklab.com/d/224

Top 100 epilepsy predictions & their chemical structure

Discuss at thinklab.com/d/224#5

Top 100 epilepsy predictions & their drug targets

Discuss at thinklab.com/d/230#14

• Hetionet (≤ v1.0) integrated data from 31 resources:
  • 5 United States Government works
  • 12 openly licensed
  • 4 non-commercial use only
  • 9 were all rights reserved
  • 1 explicitly & contractually forbid reuse
• Requested permission for 11 resources:
  • median time to first response was 16 days
  • 2 affirmative responses
• Other considerations:
  • who owns data
  • incompatibilities: share alike vs non-commercial
  • copyright status of data & fair use
• Solution: license attribute per node/relationship

Legal barriers to data reuse

Recommendations:

1. release data under an open license
2. University researchers: commit to open in your resource sharing plan