ComptoxAI: A toolkit for AI research in computational toxicology
Note: We are in the process of transitioning from Neo4j to Memgraph. Please excuse any features that don't work as expected while we complete the move!
We are also prototyping a new data model for representing individual people's environmental exposures and the way their bodies respond to those exposures. This will be a larger-scale project developed over several years, but we have an initial prototype using synthetic patient data (from the US CMS SynPUF dataset) available at the following link: oekg.comptox.ai. Note that only a few patients are included in this prototype, and we have only implemented a few types of exposures/observations. To access the database, leave the username and password fields blank and click "Connect".
ComptoxAI provides:
- A graph database for storing and retrieving data used in computational toxicology research (implemented in Neo4j)
- Algorithms for analyzing the structure of data stored in the graph database
- A full-featured OWL ontology for computational toxicology (which is used to structure and query the graph database)
- This website, which includes a blog and showcase of research resulting from ComptoxAI
- (Coming soon:) A gallery of machine learning models for making scientific discoveries from ComptoxAI data
Data in ComptoxAI are integrated from a large number of third-party open access databases. We especially want to thank our collaborators at the US Environmental Protection Agency for their help integrating data from the Adverse Outcome Pathway Database (AOP-DB) and DSSTox resources, which together comprise much of the core functionality of ComptoxAI. To see all of the data sources included in ComptoxAI, check out our About page.