Much of biomedical science focuses on discovering causal relationships in nature (e.g., the molecular causes of cancers). A current challenge is to find such relationships within terabytes and petabytes of data that are currently being generated (“big data”). The Center for Causal Discovery (CCD) is developing and disseminating algorithms that help biomedical scientists discover causal relationships from large biomedical datasets containing different types of data (e.g., genetic, protein, imaging, electronic health record, social media data).
A primary goal of the CCD is to provide software tools that assist biomedical scientists in discovering causal relationships from big biomedical datasets. As examples of application possibilities, these tools are being applied within the Center to help discover (1) driving genetic mutations that cause cancer, (2) the molecular basis of lung disease susceptibility and progression, and (3) functional connections within the human brain.
The CCD is a partnership of the University of Pittsburgh, Carnegie Mellon University, the Pittsburgh Supercomputing Center, and Yale University. It is a member of the National Institutes of Health Big Data to Knowledge (BD2K) Consortium (bd2k.nih.gov).
A news release announcing the funding of the Center can be found here: http://www.upmc.com/media/NewsRelea…rtium.aspx