For each relationship we're trying to model, we'll need to extract the Hetionet relationships. Right now we'll be using Hetionet v1.0 relationships as the only knowledge base for a relationships. In the future, we could use multiple resources as knowledge bases for a specific relationship type. Each knowledge base forms its own labeling function.
You can read all Hetionet relationships (with no relationship properties) from this TSV. It's formatted like:
Alternatively, you can make a cypher query for each relationship type to https://neo4j.het.io, like:
MATCH path = (disease:Disease)-[:ASSOCIATES_DaG]->(gene:Gene)
RETURN
disease.identifier AS disease_id,
gene.identifier AS gene_id
ORDER BY disease_id, gene_id
You can make these queries programmatically to return pandas DataFrames in Python.
For each relationship we're trying to model, we'll need to extract the Hetionet relationships. Right now we'll be using Hetionet v1.0 relationships as the only knowledge base for a relationships. In the future, we could use multiple resources as knowledge bases for a specific relationship type. Each knowledge base forms its own labeling function.
You can read all Hetionet relationships (with no relationship properties) from this TSV. It's formatted like:
Alternatively, you can make a cypher query for each relationship type to https://neo4j.het.io, like:
You can make these queries programmatically to return pandas DataFrames in Python.