CPCP is developing innovative algorithms, models and software packages that use methods from machine learning and statistical modeling to infer predictive models for biomedicine.
CPCP is addressing a multitude of challenges that arise in biomedical Big Data analysis including: the heterogeneity, high dimensionality and volume of relevant data sources, latent variables, sparse irregular sampling of longitudinal data, unstructured data, value-of-information considerations, and the need for error-rate assessment and control.
CPCP is developing and evaluating novel methods in the context of several driving biomedical problems, including breast cancer, Alzheimer's disease, asthma, stem cell development and hematopoiesis. CPCP aims to advance the state of the art in our basic understanding of the biology of these diseases and processes, and in methods for risk assessment, diagnosis, prognosis and treatment planning
The Center for Predictive Computational Phenotyping is developing innovative computational and statistical methods and software for a broad range of problems that can be cast as computational phenotyping. The term phenotype, which is derived from the Greek word phainein meaning‚ "to show" refers to the observable properties of an organism that result from the interaction of its genotype and its environment. Some phenotypes are easily measured and interpreted, and are available in an accessible format.
However, a wide range of scientifically and clinically important phenotypes do not satisfy these criteria. In such cases, computational phenotyping methods are required either to extract a relevant phenotype from a complex data source or collection of heterogeneous data sources, and to predict clinically important phenotypes before they are exhibited. The Center is investigating how to exploit a wide array of data types for these tasks, including molecular profiles, medical images, electronic health records, and population-level data.
The Center is also providing training in biomedical Big Data analysis to scientists and clinicians, and it is investigating the bioethical issues surrounding the technology being developed.
Funders & Partners
CPCP is supported by the National Institutes of Health Big Data to Knowledge (BD2K) Initiative under Award Number U54 AI117924. The CPCP partners with the Morgridge Institute for Research and the Marshfield Clinic Research Foundation, and receives additional support from the UW-Madison School of Medicine and Public Health, the UW-Madison Graduate School, and the Wisconsin Alumni Research Foundation.