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Emerging Questions in Cancer Systems Biology (U01)

Post Date

March 11th 2016

Application Due Date

November 23rd 2018

Funding Opportunity Number

PAR-16-131

CFDA Number(s)

93.395
93.396

Funding Instrument Type(s)

Cooperative Agreement

Funding Activity Categories

Education
Health

Eligibility Categories

State Governments
County Governments
City or Township Governments
Special District Governments
Independent School Districts
Public and State Controlled Institutions of Higher Education
Federally Recognized Native American Tribal Governments
Public Housing Authorities or Indian Housing Authorities
Non-Federally Recognized Native American Tribal Organizations
Non-Profits With 501 (c) (3) Status With The IRS (Except Higher Education Institutions)
Non-Profits Without 501 (c) (3) Status With The IRS (Except Higher Education Institutions)
Private Institutions of Higher Education
For-Profit Organizations (Except Small Businesses)
Small Businesses
Other

Other Eligible Applicants include the following: Alaska Native and Native Hawaiian Serving Institutions; Asian American Native American Pacific Islander Serving Institutions (AANAPISISs); Eligible Agencies of the Federal Government; Faith-based or Community-based Organizations; Hispanic-serving Institutions; Historically Black Colleges and Universities (HBCUs); Indian/Native American Tribal Governments (Other than Federally Recognized); Non-domestic (non-U.S.) Entities (Foreign Organizations); Regional Organizations; Tribally Controlled Colleges and Universities (TCCUs) ; U.S. Territory or Possession.

Funding

  • Award Range:

    $None - $None

Grant Description

The National Cancer Institute (NCI) is sponsoring a new Cancer Systems Biology Consortium (CSBC) that includes U54 CSBC Research Centers (RFA-CA-15-014), a U24 CSBC Coordinating Center (RFA-CA-15-015) and, through this FOA, well-defined, discrete and circumscribed Research Projects addressing emerging problems in cancer. The CSBC initiative aims to address challenges in cancer research through the use of experimental biology or population science combined with in silico modeling, multi-dimensional data analysis, and systems engineering.

Contact Information


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