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Background: Knowledge graphs (KGs) facilitate the representation of complex relationships among heterogeneous data types and have been used extensively in biomedical research to model biological phenomena. While many data-driven KG construction methods have been developed, they remain largely unable to:
- Construct KGs from multiple disparate data sources
- Combine KGs created by different systems
- Collaborate or share KGs across institutions due to their inability to account for the use of different schemas, standards, and vocabularies
Used extensively in life sciences research, the Semantic Web was created to resolve these types of knowledge integration problems. The Web Ontology Language (OWL) is a Semantic Web standard for a graph-based knowledge representation and reasoning framework. OWL is highly expressive, enabling the integration of heterogeneous data using explicit semantics, and allows for the generation of new knowledge using deductive logic. Unfortunately, existing OWL-based KG construction methods are often built using complicated programs or toolsets, in arcane or difficult to use programming languages and require extensive computational resources.
Solution: PheKnowLator (Phenotype Knowledge Translator), a fully automated Python 3 library explicitly designed for optimized construction of semantically-rich, large-scale biomedical KGs from complex heterogeneous data. The PheKnowLator framework provides detailed Jupyter Notebooks and scripts which greatly simplify KG construction, assisting even non-technical users through all steps of the build process.
Translational Research Informatics Team
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| Bill Baumgartner 🖥️ | Ignacio Tripodi 🖥️ | Adrianne L. Stefanski 🔬 | Jordan Wyrwa ⚕️ |
The resulting knowledge graphs and molecular mechanism embeddings are free to download and included as part of each release.
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v2.0.0- Details: v2.0.0
- Data: v2.0.0 Data Sources
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v1.0.0- Details: v1.0.0
- Data: v1.0.0 Data Sources
- We presented this work (poster) at the 15th Annual Rocky Mountain Bioinformatics Conference
- Ignacio Tripodi will present results on MechSpy, a novel application that uses PheKnowLator to perform toxicological mechanistic inference at the 2019 meeting of The American Society for Cellular and Computational Toxicology
- PheKnowLator is referenced in a review article on Knowledge-based Data Science in the biomedical domain
- PheKnowLator was mentioned in a recent blog post
- The computational performance of PheKnowLator will be presented at the 2020 annual International Conference on Intelligent Systems for Molecular Biology (submission can be found here)
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