The center for causal discovery of biomedical knowledge from big data

Abstract

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers.

Keywords: Big Data to knowledge (BD2K); biomedical knowledge; biomedical science; causal discovery; center of excellence.

Figure 1:

Center for Causal Discovery (CCD) organization and workflow optimized for the development of causal modeling and discovery (CMD) algorithms and tools designed to help address causal discovery in biomedicine from big data.

Figure 2:

Basic workflow of the causal modeling and discovery (CMD) system under development in the Center for Causal Discovery (CCD). End users will interact with wizards that help them select the appropriate methods at each step in the workflow.