Traditional disciplinary boundaries
We bring innovation across the boundaries of traditionally-defined disciplines, and combine theoretical models with data-driven machine learning approaches. Specific applications range from biomarker discovery to novel materials and financial forecasting.Explore research areas »
Biomarker discovery from high-throughput data: Variable selection methodology inspired by computational physics techniques (see toolbox overview).
Multi-disciplinary Insights has developed biomarker discovery tools that identify the smallest number of key variables most essential for a given biological context from high-throughput datasets. The algorithm is inspired by statistical physics of strongly interacting systems, and uncovers most sensitive predictor combinations for medical diagnosis and prognosis applications.
The top banner on this page presents part of the map of science visualization (move the mouse to thetop of the page to explore). This particular network of scientific fields is constructed based on a bibliographic analysis of journal citations:
This graph is a good illustration for the interconnectedness of various disciplines and their relationship to each other.
More details can be found on our structure of science page.