2:35 pm - 2:50 pm
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 (see http://het.io/repurpose).
Hetionet is available online as a public Neo4j database instance at https://neo4j.het.io. 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.
Daniel is Postdoctoral Fellow in Greene Lab at the University of Pennsylvania and 2016 Neo4j Ambassador. As a data scientist, he applies hetnets — networks with multiple node and relationship types — to understand human disease.