Artificial intelligence-curated knowledge base and knowledge graph for Nipah virus and tuberculosis

A key challenge for open drug discovery is the development and maintenance of focused knowledge bases. We will develop knowledge bases and graphs on tuberculosis (TB) pathobiology, drug-resistant TB therapies and Nipah virus pathobiology. 

We will train an ‘intelligent curation engine’ to scan relevant content sources for evidence to create knowledge bases and graphs. The knowledge bases will be updated daily and used to create specific curated content which can be accessed via news and database applications. The content will be 'read' using natural language processing (NLP) and semantic predication and the learned content will be used to create graphs that can be used directly as a knowledge source or can be linked to screening, toxicology and genetic data.  

This project will result in a TB pathobiology knowledge base and knowledge graph, a news magazine and social media feeds. We will also create a knowledge base and graph for TB adjunct therapies and Nipah virus pathobiology. These will be made available as open-content sources.