SEffNet 
SEffNet (Side Effect Network embeddings) is a tool that optimizes, trains, and evaluates predictive models for biomedical networks that contain drug-, target- and side effect-information using different network representation learning methods in an attempt to understand the causes of side effects.
This package was developed during the master's thesis of Rana Aldisi.
Structure
notebooks: Notebooks that were used for training and evaluation of models, and interpertation of prediction modelresources: The graphs and materials that are used for training and testing
Installation
seffnet can be installed on python37+ from the latest code on GitHub with:
$ pip install git+https://github.com/seffnet/seffnet.gitUsage
Using the predictive model
If you've installed seffnet locally, you can use the default model from the GitHub repository with:
from seffnet.default_predictor import predictor
# Find new relations for a given entity based on its CURIE
results = predictor.find_new_relations(curie='pubchem.compound:5095')
...You can get the embeddings for phenotype entities with
import itertools as itt
from seffnet.default_predictor import predictor
phenotype_to_embedding = {
node_data['identifier']: predictor.embeddings[node_id]
for node_id, node_data in predictor.node_id_to_info.items()
if node_data['namespace'] == 'umls'
}
# could use sklearn.metrics.pairwise.cosine_similarity on the values in this dictYou can use the default model in the CLI:
$ seffnet predict pubchem.compound:5095You can predict on new chemicals via their SMILES strings based on their similarity to chemicals included in the network. Warning: we haven't benchmarked how well this actually works yet.
$ seffnet predictc "C1=CC=C(C=C1)C2=CC=C(C=C2)CCO"Rebuilding the resources
You can rebuild all the graphs and maps created for this project by running the following:
$ seffnet rebuildNote that you need to have RDKit package and environment to be able to run this command
Model training and evaluation
You can train an NRL model using the following:
$ seffnet train --input-path ./resources/basic_graphs/fullgraph_with_chemsim.edgelist --evaluation --method node2vec- For further CLI options and parameters use --help, -h
Optimizing hyperparameters
Network representation learning models can be optimized with:
$ seffnet optimize --input-path ./resources/basic_graphs/fullgraph_with_chemsim.edgelist --method node2vec- For further CLI options and parameters use --help, -h
Web Application
The web application allows users to get results from the model programmatically. Make
sure the extra dependencies have been installed as well using the [web] extra.
Unfortunately, this doesn't work when installing directly from GitHub, so see the
setup.cfg for the Flask dependencies.
$ pip install -e .[web]Run development server with:
$ seffnet web --host localhost --port 5000Run through docker with:
$ docker-compose up- A user interface can be found at http://localhost:5000
- An auto-generated swagger UI can be found at http://localhost:5000/apidocs
As an example, you can check the chemicals predicted to interact with HDAC6 at http://localhost:5000/predict/uniprot:Q9UBN7?results_type=chemical.
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