Neglected/Infectious Disease Knowledge Systems



Interactive application with grid-based views of the Tuberculosis knowledgebase and knowledgegraph.

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Interactive application with grid-based views of the Chikungunya knowledgebase and knowledgegraph.

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Nipah Virus

Interactive application with grid-based views of the Nipah Virus knowledgebase (no knowledgegraph).

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Interactive application with grid-based views of the Coronavirus knowledgebase and knowledgegraph.

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The development of disease or topic-focused knowledge bases are critical to promote research and development in healthcare. Historically, such knowledge bases have been painstakingly curated by subject matter experts, and require years of effort to come to fruition. In addition, they require extensive curation to keep up to date with developing information and are weeks or months out of date upon their release. Here, we present the development of a disease specific knowledge base, and an associated knowledge graph, developed with minimal manual effort, and that can be built quickly and kept up to date with little manual intervention, and that is updated multiple times a day by artificial intelligence. We have leveraged advances in semantic technologies, machine learning, and NLP to enable subject matter experts to train a platform that uses AI to identify, curate, and load evidence into a disease focused knowledge base, and to predicate that content into an associated biomedical knowledge graph. This enables the rapid and cost effective deployment of disease or topic-focused knowledge platforms to empower biomedical research. You will find that that our knowledge base/knowledge graph greatly accelerates your research process, and provides powerful insights.

The Open Source Pharma Foundation and Ingentium are collaborating on the implementation of an open information commons for the collaborative development of new disease treatments using an open source methodology. Together, Wwe have created a cloud based infrastructure. This project is initially focused on Tuberculosis, Nipah Virus, and Chikungunya Virus, and takes advantage of the Ingentium machine learning tools to create knowledge bases and knowledge graphs for each of these areas. The Mayo Clinic Center for Tuberculosis, which is a WHO Collaborating Center for Digital Health and Precision Medicine for Tuberculosis, is, at the invitation of OSPF, a collaborator on the Tuberculosis knowledge base/knowledge graph. Our vision is to build living knowledge bases focused on the specific content needed to enable drug discovery and development for different neglected diseases.

You will be able to open an account on your first attempt (email and password required). Your account will be activated once you respond to the verification email. To access knowledge graph applications Neo4j and Linkurious, you will need a separate username and password. Please contact to request access or if you have any additional queries, please write to or