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Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science (R41/R42)

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Agency: Department of Health and Human Services
Program/Year: STTR / 2014
Solicitation Number: PA-14-157
Release Date: March 14, 2014
Open Date: July 5, 2014
Close Date: May 7, 2017 (closing in 1017 days)
001: Funding Opportunity Description
Description:

This Funding Opportunity Announcement (FOA) encourages Small Business Technology Transfer (STTR) grant applications from small business concerns (SBCs) that focus on development of technologies in biomedical computing, informatics, and Big Data science. This FOA is coordinated by the NIH Big Data Initiative (BD2K) and the Biomedical Information Science and Technology Initiative (BISTI) committees. Through this and related opportunities, Institutes and Centers of the NIH offer support for: fundamental research in biomedical computing, informatics, and Big Data Science; development of new software, tools and related resources; and applications of computational technologies to a particular domain area(s) in biomedical research. Information on these programs and related funding opportunities from participating Institutes and Centers of NIH can be found at http://www.bd2k.nih.gov and http://www/bisti.nih.gov. Note that in this document, the term “biomedical” will be used in the broadest sense to include biological, biomedical, behavioral, social, environmental, and clinical studies that relate to understanding health and disease. Applicants are reminded to carefully check that the proposed research lies in the mission of the participant Institutes and Centers of the initiative.

In addition to applications for funding in biomedical computing and informatics research that have been accepted under previous BISTI funding opportunity announcements, this announcement acknowledges new opportunities are also emerging as large and complex data sets are becoming increasingly available to the research community. While the biomedical research enterprise is producing increasingly large amounts of digital data, it has not yet fully capitalized on the transformative opportunities that these data provide.  As stated by the Data and Informatics Working Group (DIWG) of the Advisory Committee to the NIH Director, "Colossal changes in biomedical research technologies and methods have shifted the bottleneck in scientific productivity from data production to data management, communication, and interpretation." (http://acd.od.nih.gov/Data%20and%20Informatics%20Working%20Group%20Report.pdf).  In this context, the term "Big Data Science" is meant to capture the opportunities and address the challenges facing all biomedical researchers in releasing, accessing, managing, analyzing, and integrating datasets of diverse data types.  Such data types may include imaging, phenotypic, molecular (including –omics), physiological, anatomical, clinical, behavioral, environmental, and many other types of biological and biomedical data.  They may also include data generated for other purposes (e.g., social media, search histories, and cell phone data). 

Investigators may target one or multiple of the following four themes of biomedical computing, informatics, and Big Data science that will enable progress in biomedical research.

1. Collaborative environments and technologies: An applicant may develop enabling technologies that address the issues of releasing Big Data and tools and gaining access to and using Big Data and tools. Examples include, but are not limited to:

  • Knowledge environments
  • Research commons
  • Scalable, extensible, and maintainable methods of data and metadata curation
  • Data security and privacy, and technical areas related to other ethical, legal, and social implications of Big Data

2. Data integration: An applicant may may develop enabling technologies that address efficient and effective ways to create connections across data types (i.e., unimodal or multimodal data integration). Examples of data types that could be addressed include, but are not limited to:

  • Omics data (e.g., genomics, proteomics, metabolomics, etc.)
  • Image and physiological data (e.g., CT, PET/SPECT, sMRI, fMRI, rMRI, DTI, EEG, MEG, ultrasound, cellular level imaging, multi-electrode recording, etc.)
  • Behavioral, social, and environmental data
  • Clinical data (e.g., lab tests, pathology, imaging, diagnosis, electronic health records, etc.)
  • Data from nontraditional sources (e.g., social media, mobile devices, etc.)
  • Multiscale data (genomic, epigenomic, subcellular, cellular, network, organ, systems, organism, population levels)
  • Multiplatform data (desktop, cloud-based storage, etc.)
  • Data from multiple research areas and diseases (e.g., common inflammation pathways in cancer, obesity, immune diseases, and neurodegenerative diseases)
  • Data with special considerations (e.g., sparse data, heterogeneous data, or very large or very small datasets)
  • Human-computer interfaces and visualization

3. Analysis and modeling methodologies: An applicant may propose to develop enabling technologies and approaches for modeling, simulation, or analysis to produce useful biomedical information in ways that current methods cannot provide. Examples include, but are not limited to:

  • Processing of data to allow more efficient analyses
  • Multidimensional statistical and computational methods for analyzing, inspecting, displaying, representing, parsing, and searching high-dimensional data
  • Intensive longitudinal data analyses
  • Spatio-temporal dynamical modeling and adaptive dynamical modeling (e.g., parameter fitting and optimization of complex time series data)
  • Mechanistic modeling
  • Agent-based, Ordinary Differential Equation (ODE), Partial Differential Equation (PDE), and stochastic methods
  • Clinical decision-making
  • Individualized therapies
  • Multi-scale modeling
  • Organ-based or whole-body-based modeling
  • Population-based modeling

4. Computer science and statistical approaches: An applicant may propose to develop enabling technologies in basic computer science such as:

  • Approaches for database development and management – ways to organize, store, and query Big Data
  • Technological approaches to distributing, sharing, and compressing Big Data
  • Crowdsourcing data annotation and data management
  • Approaches for efficient and novel uses of cloud platforms

The biomedical computing, informatics, and Big Data science research and development should take place in the context of biomedical and behavioral research that is of interest across most NIH Institutes and Centers from basic biomedicine to research in all relevant organ systems and diseases. The applicant should address the intended use of the technology, identify the technology development needs that the project will address, and how these needs are related to important biomedical, translational, or clinical research problems. Applicants will be expected to demonstrate fundamental understanding and adequate expertise in the relevant areas of both biomedical research and computational science and technology. 

Through separate funding opportunity announcements of similar scientific scope, participating Institutes and Centers invite applications for early stage development in biomedical computing, informatics, and Big Data science (R01) PA-14-155, extended development, hardening and dissemination of software technology (R01) PA-14-156, as well as small business innovation research (SBIR) PA-14-154. Some NIH Institutes and Centers may have other grant mechanisms that could apply to biomedical computing projects. Applicants are encouraged to visit the BD2K and BISTI web sites for these and other relevant funding opportunities: http://www.bd2k.nih.gov/opportunities, and http://www.bisti.nih.gov/bistic_funding.cfm