NP-KG

Knowledge Graph Framework to Generate Hypotheses for Natural Product-Drug Interactions

NP-KG is a graph framework that creates a biomedical knowledge graph (KG) to identify and generate mechanistic hypotheses for pharmacokinetic natural product-drug interactions (NPDIs). NP-KG uses the PheKnowLator ecosystem to create an ontology-grounded KG. It then uses two relation extraction systems to extract triples from full texts of natural product-related scientific literature to create a literature-based graph, and integrates the nodes and edges in the ontology-grounded KG.

NP-KG Builds

NP-KG: Merged PheKnowLator KG and literature-based graph with 30 natural products.

Ontology-grounded KG: PheKnowLator KG with a few additional data sources.

Literature-based Graph: Literature-based graph constructed from scientific literature with relation extraction systems (SemRep and INDRA/REACH) and closure operations.

How to Download and Use

Setup environment

1. Clone the repository or download all files.
2. Install all required packages. Requires Python>=3.6.

python -m pip install -r requirements.txt

If you want to use the pre-built KGs

1. Download the knowledge graph and node labels files from Zenodo and add to local folder - resources/knowledge_graphs. NP-KG is available as TSV file with triples and NetworkX multidigraph (gpickle files).

• Merged KG: includes merged PheKnowLator KG and literature-based graph. Download this file if you do not know which KG to use.
  • Filename: NP-KG_v3.0.0.tsv
  • Filename: NP-KG_v3.0.0.gpickle
• PheKnowLator KG: includes full instance-based build of the PheKnowLator KG. See PheKnowLator for more details.
  • Filename: PheKnowLator_v3.1.2_full_instance_inverseRelations_OWLNETS_NetworkxMultiDiGraph.gpickle

Node Labels and Node Types

• Download nodeLabels_v3.0.0.tsv file with all node labels for the merged KG.
• Download nodeTypes_v3.0.0.tsv file with node types for all nodes in the merged KG.

2. See evaluation-scripts for examples of queries and path searches.

Note: The download link also contains the KGs as gpickle and ntriples files with the same nodes and edges that can be loaded for other applications.

Loading NP-KG with GRAPE

The Graph Representation Learning library GRAPE provides efficient graph embeddings. To load NP-KG (version 3.0.0) in GRAPE, use the from_csv function and TSV files mentioned above:

npkg = Graph.from_csv(
            node_path=<TSV node types filename>,
            node_list_node_types_column_number=1,
            nodes_column_number=0,
            node_list_separator='\t',
            node_list_header=True,
            edge_path=<TSV version of KG filename>,
            edge_list_separator='\t',
            edge_list_header=True,
            edge_list_edge_types_column_number=1,
            sources_column_number=0,
            destinations_column_number=2,
            weights_column_number=3,
            directed=True,
            verbose=True
        )

NP-KG (v1.0.1) can also be loaded with as below. See NP-KG Grape Animation tutorial for details.

pip install grape -U
from grape.datasets.zenodo import NPKG
graph = NPKG(directed=True)
graph

If you are interested in constructing or extending NP-KG

See wiki for details of data sources, construction, use cases, and evaluation.

Get In Touch

Get in touch through GitHub issues, discussion, or email!

Related Work

NP-KG Publication

Taneja SB, Callahan TJ, Paine MF, Kane-Gill SL, Kilicoglu H, Joachimiak MP, Boyce RD. Developing a Knowledge Graph Framework for Pharmacokinetic Natural Product-Drug Interactions. Journal of Biomedical Informatics. 2023. DOI: doi.org/10.1016/j.jbi.2023.104341.

AMIA Informatics Summit poster

Taneja SB, Ndungu PW, Paine MF, Kane-Gill SL, Boyce RD. Relation Extraction from Biomedical Literature on Pharmacokinetic Natural Product-Drug Interactions. Poster presentation, AMIA Informatics Summit 2022; March 21-24, 2022.

ISMB Conference Abstract and Related Files

Taneja SB, Callahan TJ, Brochhausen M, Paine MF, Kane-Gill SL, Boyce RD. Designing potential extensions from G-SRS to ChEBI to identify natural product-drug interactions. Intelligent Systems for Molecular Biology/European Conference on Computational Biology (ISMB/ECCB), 2021. https://doi.org/10.5281/zenodo.5736386

Cite this Work

Publication

@article{taneja_developing_2023,
	title = {Developing a {Knowledge} {Graph} for {Pharmacokinetic} {Natural} {Product}-{Drug} {Interactions}},
	volume = {140},
	issn = {1532-0464},
	url = {https://www.sciencedirect.com/science/article/pii/S153204642300062X},
	doi = {10.1016/j.jbi.2023.104341},
	language = {en},
	urldate = {2023-03-23},
	journal = {Journal of Biomedical Informatics},
	author = {Taneja, Sanya B. and Callahan, Tiffany J. and Paine, Mary F. and Kane-Gill, Sandra L. and Kilicoglu, Halil and Joachimiak, Marcin P. and Boyce, Richard D.},
	year = {2023},
}

Zenodo Dataset

@dataset{taneja_sanya_bathla_2024_12536780,
  author       = {Taneja, Sanya Bathla},
  title        = {{NP-KG: Knowledge Graph for Natural Product-Drug 
                   Interactions}},
  month        = jun,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {3.0.0},
  doi          = {10.5281/zenodo.12536780},
  url          = {https://doi.org/10.5281/zenodo.12536780}
}

Funding

This work is supported by the National Institutes of Health National Center for Complementary and Integrative Health Grant U54 AT008909.