is a Knowledge Graph.
A large-scale biomedical knowledge graph assembled from PubMed abstracts, containing over 22 million entities and 120 million relations.
health, biomedical, drug discovery, translational, genomics
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| ID | Name | URL | Category | Format | Description |
|---|---|---|---|---|---|
| ikraph.site | BioKDE | biokde.insilicom.com | GraphicalInterface | http | Biomedical Knowledge Discovery Engine... |
| ikraph.code | iKraph Code | iKraph | ProcessProduct | ❔ | Code for named entity recognition, re... |
| ikraph.graph | iKraph graph metadata | data.tar.gz?download=1 (58.3 MB) | GraphProduct | json | Graph metadata for iKraph, including ... |
| ikraph.graphdata | iKraph graph data | iKraph_full.tar.gz?download=1 (1.3 GB) | GraphProduct | ❔ | Complete graph data for iKraph with a... |
iKraph is a comprehensive large-scale biomedical knowledge graph developed by Insilicom for AI-powered data-driven biomedical research. It represents one of the largest structured biomedical knowledge resources assembled from literature mining.
iKraph was constructed by applying advanced natural language processing and relation extraction techniques to the entire corpus of PubMed abstracts. The knowledge graph contains over 22 million biomedical entities and 120 million relations, covering a wide range of biomedical concepts including genes, proteins, diseases, drugs, pathways, and phenotypes.
The primary goal of iKraph is to enable knowledge discovery and hypothesis generation for biomedical research and drug development. It integrates information across multiple domains to support various applications:
iKraph employs a sophisticated named entity recognition and relation extraction pipeline to process biomedical literature at scale. The pipeline includes:
The knowledge graph is accessible through the BioKDE (Biomedical Knowledge Discovery Engine) web interface, which provides search, visualization, and exploration capabilities for researchers.
iKraph has been successfully applied to several biomedical research areas:
If you use iKraph in your research, please cite:
Zhang Y, Sui X, Pan F, et al. A comprehensive large-scale biomedical knowledge graph for AI-powered data-driven biomedical research. Nature Machine Intelligence. 2025. https://doi.org/10.1038/s42256-025-01014-w
Created: July 22, 2025 | Last modified: July 22, 2025