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Workshop #1 - Hetionet
"Hetionet is a comprehensive knowledge graph that integrates diverse biomedical data to uncover relationships between diseases, drugs, genes, and other biological entities, driving deeper insights in the life sciences."
Use this link for an in-depth exploration of Hetionet: https://neo4j.het.io/browser/
Datamodel
How to use Neo4j Browser
Enter command (Cypher)
Execute command
Expand cancas
Node Navigation + Setting
Lock / Unlock
Node
Hide Node
Show relations
in the "Browser Settings"
check "Connect result nodes"
Dark / Light
Mode
Query samples for Demo
#1
Retrieve the database model
CALL db.schema.visualization();
#2
Finds all DISEASE nodes that containing "rheuma" in the the node attribut "name"
match (d:Disease)
where d.name contains "rheuma"
return d
#3
Finds all GENE nodes with a relationship type "ASSOCIATED_DaG" with the "rheumatoid arthritis" DISEASE.
match (d:Disease)-[r:ASSOCIATES_DaG]->(g:Gene)
where d.name = "rheumatoid arthritis"
return d, r, g
Some samples you can explore
#1
Get the whole picture
Retrieve the database model
CALL db.schema.visualization();
#2
Finds all DISEASE nodes that containing "rheuma" in the the node attribut "name"
match (d:Disease)
where d.name contains "rheuma"
return d
#3
Finds all GENE nodes with a relationship type "ASSOCIATED_DaG" with the "rheumatoid arthritis" DISEASE.
match (d:Disease)-[r:ASSOCIATES_DaG]->(g:Gene)
where d.name = "rheumatoid arthritis"
return d, r, g
#4
Find all genes that participate in the mitotic spindle checkpoint and are expressed in the lung
MATCH (bp:BiologicalProcess)-[x:PARTICIPATES_GpBP]-(gene:Gene)-[y:EXPRESSES_AeG]-(anatomy:Anatomy)
WHERE bp.name = 'mitotic spindle checkpoint' AND anatomy.name = 'lung'
RETURN bp,gene,anatomy,x,y
#5
Compounds that target genes involved in myelation.
Demyelination is the cause of much disability in multiple sclerosis patients. Below is a simple query to find all compounds that bind to proteins whose genes are involved in myelination:
MATCH path = (n0:BiologicalProcess)-[:PARTICIPATES_GpBP]-(n1)-[:BINDS_CbG]-(n2:Compound) WHERE n0.name = 'myelination' RETURN path
#6
The targets responsible for a side effect
Here we'll investigate a query to identify genes which cause a given side effect when targeted by a compound. Let's look at the side effect Cushingoid (C0332601). The following query identifies the genes that are commonly targeted by Cushingoid-causing compounds:
MATCH path = (n0:SideEffect)-[r1:CAUSES_CcSE]-(n1:Compound)-[r2:BINDS_CbG]-(n2:Gene) WHERE n0.name = 'Cushingoid' WITH [ size((n0)-[:CAUSES_CcSE]-()), size(()-[:CAUSES_CcSE]-(n1)), size((n1)-[:BINDS_CbG]-()), size(()-[:BINDS_CbG]-(n2)) ] AS degrees, path, n2 WITH n2, count(path) AS PC, sum(reduce(pdp = 1.0, d in degrees| pdp * d ^ -0.4)) AS DWPC RETURN n2.identifier AS gene_id, n2.name AS gene_symbol, n2.description AS gene_name, PC, DWPC ORDER BY DWPC DESC, gene_symbol
try yourself
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