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Lea Gütebier
2025-08-22 11:52:43 +02:00
commit ec27c71148
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How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
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state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
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(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
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might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.

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# BRAinS
This repo contains the code for creating the BRAinS-Graph (*Biomedical Knowledge Graph for Recommending and Analysing Health Studies*). It loads data from four sources into a Neo4j graph database:
- [ClinicalTrials.gov](https://clinicaltrials.gov/)
- [The Portal of Medical Data Models (MDM Portal)](https://medical-data-models.org/)
- [Medical Subject Headings (MeSH)](https://www.nlm.nih.gov/mesh/meshhome.html)
- [Unified Medical Language System (UMLS)](https://www.nlm.nih.gov/research/umls/index.html)
## Structure of the repository
The repository consists of three dataloaders (`study2neo4j`, `moi`, `umls2neo4j`) and a postprocessing script (`postprocessing`).
> [!NOTE]
> The dataloader for the MDM Portal, `mdm2neo4j`, can be found in an additional repository: [mdm2neo4j repo](https://git.uni-greifswald.de/MILA_public/mdm2neo4j).
> It should be run as well to create the BRAinS-Graph.
### File Structure
```
src/
│
├── study2neo4j
│ ├── run.py # Main script to execute the ClinicalTrials.gov import
│ ├── ct2neo4j.py # Functions for database connecting and data importing
│ └── README.md # Documentation for setup and usage
│
├── moi
│ ├── moi.py # Main script to execute the ontology import and processing
│ ├── methods.py # Functions for configuring, importing, and processing the graph
│ └── README.md # Documentation for setup and usage
│
├── umls2neo4j
│ ├── umls2neo4j.py # Main script to execute the umls import
│ ├── methods_umls2neo4j.py # Functions for loading cui names, parsing relations, loading into neo4j
│ └── README.md # Documentation for setup and usage
│
└── postprocessing
└── postprocess.py # Main script for postprocessing (creating relationships between data sources)
```
## How to
Create a configuration file, storing your details for the database connection, e.g., in your home-directory with the name `brains.conf`.
It should have the following structure:
```ini
[neo4j]
uri = bolt://localhost:7687
username = neo4j
password = myfancypassword
```
Run the dataloaders `study2neo4j`, `moi`, `umls2neo4j`, which can be found under :file_folder: `src`.
Instructions for usage are provided in the individual README.md files.
Run the dataloader [mdm2neo4j repo](https://git.uni-greifswald.de/MILA_public/mdm2neo4j).
> [!IMPORTANT]
> In general, the individual dataloader (`study2neo4j`, `moi`, `umls2neo4j`) can be run in any order.
> The postprocessing only works if data has been loaded into the Neo4j. It should be run last (after all dataloaders have been run).
> Make sure not to forget to run mdm2neo4j as a dataloader.
## Requirements
- make sure `python3` is installed
- install the required libraries with `pip install -r requirements.txt`
- have a running Neo4j DB (Neo4j version 5)
- create the configuration file as described in the [How To section](#how-to)
Have a running Neo4j instance with APOC and neosemantics installed.
## Licence
This program is released under [Version 3 of the GPL or any later version](https://www.gnu.org/licenses/gpl-3.0.en.html).

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argparse==1.4.0
ConfigParser==7.1.0
logging
neo4j==5.18.0

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# MOI: MeSH Ontology Importer for Neo4j
MOI imports a MeSH ontology file into a Neo4j graph database and performs postprocessing for consistent labeling and property handling.
## Quickstart
Create a configuration file with your Neo4j connection details.
For example, save it in your home directory as `moi.conf`:
```ini
[neo4j]
uri = bolt://localhost:7687
username = neo4j
password = myfancypassword
```
Start the program by providing the location of your configuration file and the folder containing the ontology files:
```sh
python3 src/moi.py --conf ~/moi.conf --files ~/mesh_files
```
## Requirements
* make sure python3 is installed
* install the required libraries with `pip install -r requirements.txt`
* have a running Neo4j instance (version 5)
* the Neo4j instance must have the Neosemantics and APOC plugin installed
* ontology files must be provided in one of the supported formats: `.owl`, `.xrdf`, `.ttl`, `.nt`
* create the configuration file as described in the Quickstart section
## General structure of the repository
The two main scripts that do things are:
`methods_moi.py` – contains helper functions for:
* creating the Neo4j graph configuration (constraints, n10s settings)
* importing ontology files into Neo4j
* postprocessing imported MeSH nodes (renaming labels, flattening properties)
`moi.py` – the main scripts:
* loads configuration
* iterates over ontology files
* *runs the import and postprocessing steps

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# This script contains methods for moi.py, initial draft from the work of Jana Cyrus
import re
import os
# create_graph_config defines the graph configuration needed for Neosemantics and creates the graph configuration if it does not exist, if a graph configuration already exists it is expected to be the same as defined here
def create_graph_config(graph):
create_constraint = '''
CREATE CONSTRAINT n10s_unique_uri IF NOT EXISTS
FOR (r:Resource) REQUIRE r.uri IS UNIQUE;
'''
check_config = 'MATCH (n:_GraphConfig) RETURN count(n) AS config_count;'
create_config = '''
CALL n10s.graphconfig.init({
subClassOfRel: "subClassOf",
handleVocabUris: "MAP",
handleMultival: "ARRAY"
});
'''
result = graph.run(check_config)
config_exists = result.single()['config_count']
if config_exists == 0:
graph.run(create_constraint)
graph.run(create_config)
def import_ontology(path, file, graph):
full_path = os.path.join(path, file).replace(os.sep, '/')
file_uri = f"file:///{full_path}"
# Detect file format
if re.match(r'.+\.(owl|xrdf)$', file):
format_type = "RDF/XML"
elif re.match(r'.+\.(ttl|nt)$', file):
format_type = "Turtle"
else:
print(f"Unsupported file format for: {file}")
return
import_query = '''
CALL n10s.rdf.import.fetch($file_uri, $format_type)
'''
print(f"Importing ontology: CALL n10s.rdf.import.fetch('{file_uri}', '{format_type}')")
graph.run(import_query, file_uri=file_uri, format_type=format_type)
# post-processing for MeSH ontology, labels are altered to identify the nodes as MeSH entries and single value properties are extracted from their arrays
def postprocess_mesh(graph):
# match all labels from MeSH
# change labels by adding ontology name in front of the original label and removing the original label
get_onto_labels = '''
MATCH (n:Resource) WHERE n.uri CONTAINS "/MESH/"
UNWIND labels(n)[1..] AS label
RETURN COLLECT(DISTINCT label) AS labels
'''
labels_result = graph.run(get_onto_labels)
onto_labels = labels_result.single()['labels']
# change label
for label in onto_labels:
change_label = f'''
CALL apoc.periodic.iterate(
"MATCH (n:{label}) WHERE n.uri CONTAINS '/MESH/' RETURN n",
"SET n:Mesh{label} REMOVE n:{label}",
{{batchSize: 10000, parallel: true}}
)
'''
print(f"Updating label: {change_label}")
graph.run(change_label)
# return property keys as list to extract properties from array
get_property_keys = '''
MATCH (n:Resource)
WHERE any(label IN labels(n) WHERE label STARTS WITH "Mesh")
WITH KEYS(n) AS keys
UNWIND keys AS key
RETURN COLLECT(DISTINCT key) AS property_keys
'''
keys_result = graph.run(get_property_keys)
property_keys = keys_result.single()['property_keys']
# extract single value properties from array
for key in property_keys:
if key == "uri":
continue
extract_properties = f'''
CALL apoc.periodic.iterate(
"MATCH (n:Resource)
WHERE any(label IN labels(n) WHERE label STARTS WITH 'Mesh')
AND size(n.{key}) = 1
RETURN n",
"WITH n UNWIND n.{key} AS prop SET n.{key} = prop",
{{batchSize: 10000, parallel: true}}
)
'''
print(f"Extracting property: {extract_properties}")
graph.run(extract_properties)

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# This integrates MeSH into Neo4j, initial draft adapted from the work of Jana Cyrus
from neo4j import GraphDatabase
import argparse
import configparser
import os
from methods_mesh import create_graph_config, import_ontology, postprocess_mesh
# define parameters - pass ontology file dir and db conf as arguments when running the script
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--conf', required=True, type=str,
help='Configuration file with database connection parameters')
parser.add_argument('-f', '--files', required=True, type=str, help='Directory with ontology files')
# parse parameters
args = parser.parse_args()
mesh_file_path = args.files
conf_file = configparser.ConfigParser()
conf_file.read(args.conf)
uri = conf_file['neo4j']['uri']
username = conf_file['neo4j']['username']
password = conf_file['neo4j']['password']
if __name__ == "__main__":
driver = GraphDatabase.driver(uri, auth=(username, password))
with driver.session() as session:
create_graph_config(session)
# Process all ontology files in the specified directory
for file in os.listdir(mesh_file_path):
if file.endswith(('.owl', '.xrdf', '.ttl', '.nt')):
import_ontology(mesh_file_path, file, session)
postprocess_mesh(session)
driver.close()

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# postprocessing
`postprocess.py` executes Cypher queries to create mappings between MeSH, UMLS, MDM, and ClinicalTrials.gov data in a Neo4j graph database.
The main script `postprocess.py` runs a set of predefined Cypher queries for:
* mapping MeSH terms to UMLS concepts
* mapping MDM Portal aliases to UMLS concepts
* mapping ClinicalTrials.gov studies to MeSH terms
* mapping ClinicalTrials.gov studies to MDM Portal entries
## Quickstart
Create a configuration file with your Neo4j connection details.
For example, save it as `postprocess.conf`:
```ini
[neo4j]
uri = bolt://localhost:7687
username = neo4j
password = myfancypassword
```
Run the postprocessing by providing the configuration file:
```sh
python3 src/postprocess.py --conf ~/postprocess.conf
```
## Requirements
* make sure `python3` is installed
* have a running Neo4j instance (version 5)
* the Neo4j instance must have the APOC plugin installed
* create the configuration file as described in the [Quickstart](#quickstart) section

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from neo4j import GraphDatabase
import logging
import argparse
import configparser
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# define funtion for running cypher queries against a neo4j
def run_cypher_query_for_postprocessing(uri, user, password, cypher_query):
try:
with GraphDatabase.driver(uri, auth=(user, password)) as driver:
with driver.session() as session:
session.run(cypher_query)
logger.info("Postprocessing query executed successfully.")
except Exception as e:
logger.error(f"Failed to execute query: {e}")
raise
# cypher queries for postprocessing
# mapping MeSH terms to UMLS concepts
cypher_mesh_to_umls = """
MATCH (m:MeshClass)
WITH collect(distinct m.cui) AS mesh_cui_lists
WITH apoc.coll.toSet(apoc.coll.flatten(mesh_cui_lists)) AS mesh_cui_set
MATCH (a:UMLSconcept)
WHERE a.cui IN mesh_cui_set
MERGE (m)-[:MESH_TO_UMLS_MAPPING]->(a)
"""
# mapping MeSH terms to UMLS concepts, optimised for larger dataset
cypher_mesh_to_umls_optimised = """
CALL apoc.periodic.iterate(
"MATCH (m:MeshClass) RETURN m",
"UNWIND m.cui AS mesh_cui
MATCH (a:UMLSconcept {cui: mesh_cui})
MERGE (m)-[:MESH_TO_UMLS_MAPPING]->(a)",
{batchSize:1000, parallel:false}
);
"""
# mapping MDM Portal Alias to UMLS concepts
cypher_mdm_to_umls = """
MATCH (a:Alias)
WITH a, a.Name as aliasname
MATCH (u:UMLSconcept)
WHERE u.cui = aliasname
MERGE (a)-[:MAPS_TO_UMLS_ENTRY]->(u)
"""
# mapping ClinicalTrials.gov studies to MeSH terms
cypher_ct_to_mesh = """
MATCH (c:MeshClass)
WITH c, c.notation as cnote
MATCH (m:meshes)
WHERE m.id = cnote
MERGE (m)-[:MAPS_TO_MESH_ENTRY]->(c)
"""
# mapping ClinicalTrials.gov studies to MDM Portal entries
cypher_ct_to_mdm = """
MATCH (s:Study)
WITH s
MATCH (c:ClinicalTrialsEntry)
WHERE c.str_id = s.OID
MERGE (s)-[r:BELONGS_TO]->(c);
MATCH (s:Study)
WITH s
MATCH (c:ClinicalTrialsEntry)
WHERE c.str_id = s.NCT_ID
MERGE (s)-[r:BELONGS_TO]->(c)
"""
# define parameters - pass json files dir and db conf as arguments when running the script
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--conf', required=True, type=str,
help='Configuration file with database connection parameters')
# parse parameters
args = parser.parse_args()
conf_file = configparser.ConfigParser()
conf_file.read(args.conf)
uri = conf_file['neo4j']['uri']
username = conf_file['neo4j']['username']
password = conf_file['neo4j']['password']
if __name__ == "__main__":
# postprocess mesh to umls
run_cypher_query_for_postprocessing(uri=uri, user=username, password=password, cypher_query=cypher_mesh_to_umls_optimised)
# postprocess clinicaltrials.gov to mesh
run_cypher_query_for_postprocessing(uri=uri, user=username, password=password, cypher_query=cypher_ct_to_mesh)
# postprocess mdm to umls
run_cypher_query_for_postprocessing(uri=uri, user=username, password=password, cypher_query=cypher_mdm_to_umls)
# postprocess clinicaltrials.gov to mdm
run_cypher_query_for_postprocessing(uri=uri, user=username, password=password, cypher_query=cypher_ct_to_mdm)

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# study2neo4j
This repo is for integrating data from ClinicalTrials.gov into Neo4j.
## Quickstart
Create a configuration file, storing your details for the database-connection.
E.g. in your home-directory with the name `study.conf`.
```ini
[neo4j]
uri = bolt://localhost:7687
username = neo4j
password = myfancypassword
```
Start the program by providing the location of your configuration-file and the location of the json files.
```sh
python3 src/run.py --conf ~/study.conf --files ~/Desktop/datasource
```
## Requirements
- make sure, `python3` is installed
- install the required libraries with `pip install -r requirements.txt`
- download all json files from [ClinicalTrials.gov]( https://clinicaltrials.gov/ ) and place all json files you want to load in one folder
- have a running Neo4j DB (Neo4j version 5)
- create the configuration-file as described in the [Quickstart section](#quickstart)
## Acknowledgements
`ct2neo4j.py` is adapted from the MeDaX pipeline (see [1], [2]) graphCreation, with permission from the authors.
[1] Gebhardt, T., Mazein, I., Michaelis, L., Henkel, R., Lobentanzer, S., Waltemath, D., & Wodke, J. (2025). MeDaX pipeline (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.15229077
[2] Mazein, I., Gebhardt, T., Zinkewitz, F., Michaelis, L., Braun, S., Waltemath, D., Henkel, R., & Wodke, J. A. (2024). MeDaX: A Knowledge Graph on FHIR. In Digital Health and Informatics Innovations for Sustainable Health Care Systems (pp. 367-371). IOS Press.

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import json
import os
from neo4j import GraphDatabase
# Define a function to add nodes and relationships recursively
def add_nodes_from_dict(tx, parent_node_label, parent_node_str_id, current_dict):
for key, value in current_dict.items(): # iterate over each key-value pair in dictionary
if key == "phases":
# Create a node for each phase
for index, phase in enumerate(value):
phase_node_str_id = f"{parent_node_str_id}_{key}_{index}"
tx.run(f"MERGE (n:phase {{str_id: $str_id, name: $phase_name}})",
str_id=phase_node_str_id, phase_name=phase)
tx.run(
f"MATCH (a:{parent_node_label} {{str_id: $parent_str_id}}), (b:phase {{str_id: $child_str_id}}) "
f"MERGE (a)-[:{key}]->(b)",
parent_str_id=parent_node_str_id,
child_str_id=phase_node_str_id
)
if isinstance(value, dict): # if value of key is a dict, then create new node:
# Create a new node for the nested dictionary
new_node_str_id = f"{parent_node_str_id}_{key}" # concatenate the parent_node_str_id and key to a new id
tx.run(f"MERGE (n:{key} {{str_id: $str_id}})", str_id=new_node_str_id) # create node with key as label
# Create a relationship from the parent node to the new node
tx.run(f"MATCH (a:{parent_node_label} {{str_id: $parent_str_id}}), (b:{key} {{str_id: $child_str_id}}) "
f"MERGE (a)-[:{key}]->(b)",
parent_str_id=parent_node_str_id,
child_str_id=new_node_str_id) # create rel betw parent and newly created node
# Recurse into the nested dictionary
add_nodes_from_dict(tx, key, new_node_str_id, value)
elif isinstance(value, list): # if value of key is a list, then check if list contains dictionary
if key == "conditions":
# Create a node for each condition
for index, condition in enumerate(value):
condition_node_str_id = f"{parent_node_str_id}_{key}_{index}"
tx.run(f"MERGE (n:condition {{str_id: $str_id, name: $condition_name}})",
str_id=condition_node_str_id, condition_name=condition)
tx.run(
f"MATCH (a:{parent_node_label} {{str_id: $parent_str_id}}), (b:condition {{str_id: $child_str_id}}) "
f"MERGE (a)-[:{key}]->(b)",
parent_str_id=parent_node_str_id,
child_str_id=condition_node_str_id
)
elif key == "keywords":
# Create a node for each keyword
for index, keyword in enumerate(value):
keyword_node_str_id = f"{parent_node_str_id}_{key}_{index}"
tx.run(f"MERGE (n:keyword {{str_id: $str_id, name: $keyword_name}})", str_id=keyword_node_str_id,
keyword_name=keyword)
tx.run(
f"MATCH (a:{parent_node_label} {{str_id: $parent_str_id}}), (b:keyword {{str_id: $child_str_id}}) "
f"MERGE (a)-[:{key}]->(b)",
parent_str_id=parent_node_str_id,
child_str_id=keyword_node_str_id
)
# if list doesn't contain any nested dictionaries, make it a value in the node
if not any(isinstance(item, dict) for item in value):
# If the list contains only primitive values (like strings or numbers),
# these values are set as properties of the parent node.
tx.run(f"MATCH (n:{parent_node_label} {{str_id: $str_id}}) SET n.{key} = $value",
str_id=parent_node_str_id, value=value)
else: # if list contains dictionaries, then a new node is created for each dict
# Process each dictionary in the list
for index, item in enumerate(value):
if isinstance(item, dict):
item_node_str_id = f"{parent_node_str_id}_{key}_{index}"
tx.run(f"MERGE (n:{key} {{str_id: $str_id}})", str_id=item_node_str_id)
tx.run(
f"MATCH (a:{parent_node_label} {{str_id: $parent_str_id}}), (b:{key} {{str_id: $child_str_id}}) "
f"MERGE (a)-[:{key}]->(b)", parent_str_id=parent_node_str_id,
child_str_id=item_node_str_id)
add_nodes_from_dict(tx, key, item_node_str_id, item)
else:
# if value is a reference
if key == 'reference':
new_node_str_id = f"{parent_node_str_id}_{key}"
tx.run(f"MERGE (n:{key} {{str_id: $str_id}})", str_id=new_node_str_id)
tx.run(f"MATCH (a:{parent_node_label} {{str_id: $parent_str_id}}), (b:{key} {{str_id: $child_str_id}}) "
f"MERGE (a)-[:{key}]->(b)", parent_str_id=parent_node_str_id, child_str_id=new_node_str_id)
tx.run(f"MATCH (n:{key} {{str_id: $str_id}}) SET n.{key} = $value",
str_id=new_node_str_id, value=value)
else:
# For non-dict and non-list values, add them as attributes to the parent node
tx.run(f"MATCH (n:{parent_node_label} {{str_id: $str_id}}) SET n.{key} = $value",
str_id=parent_node_str_id, value=value)
# Connect to Neo4j and create the graph
def create_graph_from_directory(uri, user, password, directory_path):
driver = GraphDatabase.driver(uri, auth=(user, password))
for filename in os.listdir(directory_path):
if filename.endswith('.json'):
file_path = os.path.join(directory_path, filename)
try:
with open(file_path, 'r') as file:
json_data = json.load(file)
with driver.session() as session:
root_node_label = 'ClinicalTrialsEntry'
root_node_str_id = json_data['protocolSection']['identificationModule']['nctId']
session.execute_write(
lambda tx: tx.run(f"MERGE (n:{root_node_label} {{str_id: $str_id}})", str_id=root_node_str_id))
session.execute_write(add_nodes_from_dict, root_node_label, root_node_str_id, json_data)
print(f"Successfully imported: {filename}")
except Exception as e:
print(f"Failed to import {filename}: {e}")
driver.close()

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import argparse
import logging
import configparser
from ct2neo4j import create_graph_from_directory
STUDY2NEO4J_VERSION: str = "0.1"
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
logger.info('study2neo4j v'+STUDY2NEO4J_VERSION)
# define parameters - pass json files dir and db conf as arguments when running the script
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--conf', required=True, type=str,
help='Configuration file with database connection parameters')
parser.add_argument('-f', '--files', required=True, type=str, help='Directory with json files')
# parse parameters
args = parser.parse_args()
json_file_path = args.files
conf_file = configparser.ConfigParser()
conf_file.read(args.conf)
uri = conf_file['neo4j']['uri']
username = conf_file['neo4j']['username']
password = conf_file['neo4j']['password']
# start study2neo4j
if __name__ == "__main__":
create_graph_from_directory(uri=uri, user=username, password=password, directory_path=json_file_path)

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# umls2neo4j: UMLS to Neo4j Importer
This Python script parses selected relationships from the UMLS Metathesaurus (`MRREL.RRF` and `MRCONSO.RRF`) and loads them into a Neo4j graph database.
> [!IMPORTANT]
> Requires a UMLS licence!
## Features
- Filters and loads `PAR` (parent) and `CHD` (child) relationships from `MRREL.RRF`
- Loads only preferred English concept names from `MRCONSO.RRF`
## Quickstart
Create a configuration file, storing your details for the database-connection.
E.g. in your home-directory with the name `umls.conf`.
```ini
[neo4j]
uri = bolt://localhost:7687
username = neo4j
password = myfancypassword
```
Start the program by providing the location of your configuration-file and the location of the UMLS-files.
```sh
python3 src/umls2neo4j.py --conf ~/umls.conf --mrconsofiles ~/umls/MRCONSO.RRF --mrrelfiles ~/umls/MRREL.RRF
```
## Requirements
- make sure, `python3` is installed
- install the required libraries with `pip install -r requirements.txt`
- download the UMLS Metathesaurus files (`MRREL.RRF`, `MRCONSO.RRF`) from []( ) → requires a UMLS licence
- have a running Neo4j DB (Neo4j version 5), with APOC installed
- create the configuration-file as described in the [Quickstart section](#quickstart)
## Detailled Infos
The script will:
1. Load preferred English concept names from `MRCONSO.RRF`
2. Parse allowed relationships from `MRREL.RRF`
3. Insert nodes and relationships into Neo4j using chunked batches
## Customisation
- Adjust `ALLOWED_RELS` in the script to include more relationship types
- Tune `batch_chunk_size` and `apoc_batch_size` for better performance

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import csv
from neo4j import GraphDatabase
# Load concept names from MRCONSO.RRF
# Join CUI2 (MRREL) to CUI (MRCONSO) where TS=P, STT=PF, ISPREF=Y, LAT=ENG
def load_cui_names(file_path): # this only stores names for cuis (information about source_vocab and source_vocab_code needs to be included as well)
cui_infos = {}
with open(file_path, "r", encoding="utf-8") as f:
reader = csv.reader(f, delimiter="|")
for row in reader:
cui = row[0]
language = row[1] # LAT=ENG
term_status = row[2] # TS=P (Preferred)
stt = row[4] # STT=PF (Preferred form of term)
ispref = row[6] # ISPREF=Y
source_vocab = row[11]
source_vocab_code = row[13]
name = row[14]
srl_license = row[15] # should be 0
if language != "ENG":
continue
if term_status == 'P' and stt == 'PF' and ispref == 'Y':
if srl_license == '0':
# cui_infos[cui] = name
cui_infos[cui] = {
"name": name,
"source_vocab": source_vocab,
"source_vocab_code": source_vocab_code
}
return cui_infos
# Load MRREL.RRF and return filtered concept relationships
def parse_mrrel(file_path, cui_infos, allowed_rels):
mrrelationships = []
with open(file_path, "r", encoding="utf-8") as f:
reader = csv.reader(f, delimiter="|")
for row in reader:
cui1 = row[0]
concept_type1 = row[2] # should be CUI
rel = row[3] # relationship, e.g. PAR, CHD
cui2 = row[4]
concept_type2 = row[6] # should be CUI
rel_attribute = row[7]
source_vocab = row[10] # source vocabulary
if rel in allowed_rels:
# if concept_type1 == 'CUI' and concept_type2 == 'CUI':
from_info = cui_infos.get(cui1)
to_info = cui_infos.get(cui2)
if from_info and to_info:
mrrelationships.append({
"from": cui1,
"from_name": from_info["name"],
"from_source_vocab": from_info["source_vocab"],
"from_source_vocab_code": from_info["source_vocab_code"],
"to": cui2,
"to_name": to_info["name"],
"to_source_vocab": to_info["source_vocab"],
"to_source_vocab_code": to_info["source_vocab_code"],
"type": rel,
"attribute": rel_attribute,
"source": source_vocab
})
return mrrelationships
def chunked(data, size):
for i in range(0, len(data), size):
yield data[i:i + size]
# Load relationships into Neo4j
def load_into_neo4j(rels, uri, user, password, batch_chunk_size, apoc_batch_size):
driver = GraphDatabase.driver(uri, auth=(user, password), connection_timeout=60, max_connection_lifetime=3600)
with driver.session(database="neo4j") as session:
# create constraint on cui
# session.run("CREATE CONSTRAINT IF NOT EXISTS FOR (u:UMLSconcept) REQUIRE u.cui IS UNIQUE")
chunk_count = 0
for chunk in chunked(rels, batch_chunk_size):
chunk_count += 1
print(f"Inserting chunk {chunk_count} with {len(chunk)} relationships...")
session.run("""
CALL apoc.periodic.iterate(
'UNWIND $chunk AS rel RETURN rel',
'
MERGE (c1:UMLSconcept {cui: rel.from})
SET c1.name = rel.from_name, c1.source = rel.from_source_vocab, c1.sourcecode = rel.from_source_vocab_code
MERGE (c2:UMLSconcept {cui: rel.to})
SET c2.name = rel.to_name, c2.source = rel.to_source_vocab, c2.sourcecode = rel.to_source_vocab_code
MERGE (c1)-[r:UMLS_RELATION {type: rel.type}]->(c2)
SET r.source = rel.source, r.attribute = rel.attribute
',
{
batchSize: $apoc_batch_size,
parallel: false,
params: {chunk: $chunk}
}
)
""", {
"chunk": chunk,
"apoc_batch_size": apoc_batch_size
})
print(f"All {chunk_count} batches inserted")
driver.close()

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import csv
from neo4j import GraphDatabase
import argparse
import configparser
from methods_umls2neo4j import load_cui_names, parse_mrrel, load_into_neo4j
# define parameters - pass db conf and UMLS files directory as arguments when running the script
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--conf', required=True, type=str,
help='Configuration file with database connection parameters')
# Path to MRCONSO.RRF file
parser.add_argument('-fc', '--mrconsofiles', required=True, type=str, help='Path to UMLS MRCONSO.RRF file')
# Path to MRREL.RRF file
parser.add_argument('-fr', '--mrrelfiles', required=True, type=str, help='Path to UMLS MRREL.RRF file')
# parse parameters
args = parser.parse_args()
mrconso_file = args.mrconsofiles
mrrel_file = args.mrrelfiles
conf_file = configparser.ConfigParser()
conf_file.read(args.conf)
uri = conf_file['neo4j']['uri']
username = conf_file['neo4j']['username']
password = conf_file['neo4j']['password']
# Define relationships of interest
ALLOWED_RELS = {"PAR", "CHD"}
# Run everything
if __name__ == "__main__":
print("Loading CUI information...")
name_map = load_cui_names(file_path=mrconso_file)
print(f"Loaded information about {len(name_map)} CUIs")
print("Parsing relationships...")
relationships = parse_mrrel(file_path=mrrel_file, cui_infos=name_map, allowed_rels=ALLOWED_RELS)
print(f"Parsed {len(relationships)} relationships")
print("Loading into Neo4j...")
load_into_neo4j(rels=relationships, uri=uri, user=username, password=password, batch_chunk_size=5000, apoc_batch_size=1000)
print("Finished loading into Neo4j")