Big Data to Knowledge Centers
BD2K funded 13 Centers of Excellence, which are large-scale projects developing new approaches, methods, software tools, and related resources, and are also providing training to advance Big Data science in the context of their biomedical area of focus. The Centers are located all across the United States and function with the other BD2K grantees as a consortium and collaborate with one another for the purpose of furthering every aspect of the field of biomedical data science research.
The BD2K Centers have developed a wide array of tools and resources. A list of these resources is maintained by the BD2K Centers Coordination Center (BD2KCCC). The BD2KCCC helps to promote collaboration among the Centers and across the BD2K program, and coordinates BD2K Centers Consortium activities.
Description of individual centers
Big Data for Discovery Science (BDDS)
Researchers at the BDDS focus on proteomics, genomics, and images of cells and brains collected from patients and subjects across the globe. They enable detection of patterns, trends, and relationships among these data for the efficient large-scale analysis of biomedical data.
BD2K-LINCS Data Coordination and Integration Center (BD2K-LINCS DCIC)
The BD2K-LINCS DCIC conducts data science research focused on perturbation-response data obtained from experiments with human cells and tissues, and provides access to and analysis of this data by the broader biomedical research community.
Center for Big Data in Translational Genomics (BDTG)
The BDTG creates data models and analysis tools to analyze massive datasets of genomic information to uncover the contribution of gene variants to disease with an initial focus on cancer.
Center for Causal Modeling and Discovery of Biomedical Knowledge from Big Data (CCD)
Center for Causal Modeling & Discovery of Biomedical Knowledge from Big Data (CCD) develops computational methods known as causal discovery algorithms that can be used to discover causal relationships from a combination of observational data, experimental data, and prior knowledge.
Center for Expanded Data Annotation and Retrieval (CEDAR)
Center for Expanded Data Annotation and Retrieval (CEDAR) is building new web-based technology to make it easier for biomedical scientists to author detailed metadata that describe their experiments completely, adhere to appropriate community-based standards, and incorporate controlled terms that facilitate interoperability with other online data sets.
Center for Mobility Data Integration to Insight (The Mobilize Center)
The Mobilize Center is analyzing movement data from over 6 million individuals using a smartphone app, revealing new insights about physical activity levels around the world and the factors predictive of these activity levels.
Center for Predictive Computational Phenotyping (CPCP)
The CPCP aims to accelerate the impact of predictive modeling on clinical practice by developing computational and statistical methods and software for a range of computational phenotyping tasks, including extracting relevant phenotypes from complex data sources and predicting clinically important phenotypes before they are exhibited.
Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K)
Researchers at MD2K develop tools to make it easier to gather, analyze, and interpret data from mobile and wearable sensors to reliably quantify physical, biological, behavioral, social, and environmental factors that contribute to health and disease risk.
ENIGMA Center for Worldwide Medicine, Imaging, and Genomics (ENIGMA)
The ENIGMA Center develops computational methods for integration, clustering, and learning from complex biodata types to help identify factors that either resist or promote brain disease, or assist in the diagnosis and prognosis, as well as new mechanisms and drug targets for mental health care.
Heart BD2K, a Community Effort to Translate Protein Data to Knowledge: An Integrated Platform (Heart BD2K)
The goal of the Heart BD2K Center is to democratize data research to include non-computational scientists and individuals and to apply innovative global community-driven data integration and modeling methods to address challenges involved in the study of protein structure, function, and networks with a focus on cardiovascular research.
KnowEng, a Scalable Knowledge Engine for Large-Scale Genomic Data (KnowEng)
The KnowEng Center built a computational Knowledge Engine that uses data mining and machine learning techniques to obtain and combine gene function and gene interaction information from disparate genomic data sources.
Patient-Centered Information Commons: Standardized Unification of Research Elements (PIC-SURE)
Investigators at the PIC-SURE Center develop systems to combine genetic, environmental, imaging, behavioral, and clinical data on individual patients from multiple sources into integrated sets to enable more accurate classification of individual disease or disease risk;and facilitate greater precision in patient disease prevention and treatment strategies.
This page last reviewed on October 5, 2017