About DXKB

The Disease X Knowledge Base (DXKB) is an information system designed to support the discovery and development of immunogens for vaccine design, focusing on viral pathogens that may cause outbreaks of human disease. The DXKB contains curated data, custom visualizations, services, and artificial intelligence-based models intended to accelerate vaccine development. DXKB is hosted by researchers at the University of Chicago Consortium for Advanced Science and Engineering (CASE). The resource is freely available to the public.

Funding

The DXKB is supported by the Coalition for Epidemic Preparedness Innovations (CEPI) under the Disease X Program. We gratefully acknowledge CEPI’s commitment to advancing global health security and its pivotal role in funding initiatives aimed at preventing and controlling infectious disease outbreaks.

Leadership

DXKB is a component of the larger Vaccine Immunogen Prediction neural network for Disease X (VIPnetX) award to the Houston Methodist Research Institute under Principal Investigator, Jimmy Gollihar. The project includes collaborators from Houston Methodist Research Institute, University of Chicago, University of Texas Austin, University of Texas Medical Branch, J. Craig Venter Institute, and La Jolla Institute for Immunology.

Jimmy Gollihar, Lead PI
Professor of Pathology and Genomic Medicine
Houston Methodist Academic Institute

Rick Stevens, DXKB Site PI
Professor of Computer Science
University of Chicago

James Davis, DXKB co-project lead
Computational Biologist
University of Chicago CASE

Arvind Ramanathan, DXKB co-project lead
Computational Biologist
University of Chicago CASE

Mission

The DXKB is intended to provide an integration of curated data, tools, and AI models that aid in accelerating research and shortening the development time for creating vaccines for emerging viruses, consistent with the CEPI 100 days mission.

To achieve this, DXKB integrates high quality structured bioinformatic data, literature data, experiments, AI models, and other related information for select viral families of interest and their close relatives. The resource also serves as starting point for research into the development of Retrieval Augmented Generation (RAG) models for accessing scientific data, and the development of agentic workflows for answering complex scientific questions.

Funding

To rapidly achieve the goal of building and supporting a knowledge base that can accelerate vaccine design for emerging pathogens, the DXKB site is built upon the pre-existing back-end database and service model developed by the Bacterial and Viral Bioinformatics Resource Center (BV-BRC). The BV-BRC is a freely-available and FAIR compliant resource supported by a U24 award from NIAID.