Our regularized linear models, machine learning tools, and high-throughput computational infrastructure enable efficient and reproducible (near) real-time processing, analysis and prediction using extremely large, complex and heterogeneous datasets. This enables open-science discovery, tool interoperability, and advanced statistical analysis that can be generalized to many big biomedical data-intense studies.
We have built a generic machine learning based infrastructure for modeling and interrogation of diverse arrays of data-intense biomedical and healthcare challenges. We validated the technique on neuroimaging-genetic studies throughput the age spectrum in health and disease. BDDS tools such as Deriva, BDBags and Minids are being used by this project.