OpportunityTitle: Transformative Artificial Intelligence and Machine Learning Based Strategies to Identify Determinants of Exceptional Health and Life Span (R21/R33 Clinical Trial Not Allowed)
AdditionalInformationOnEligibility: 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; Non-domestic (non-U.S.) Entities (Foreign Institutions) are not eligible to apply.  Non-domestic (non-U.S.) components of U.S. Organizations are eligible to apply.  Foreign components, as defined in the NIH Grants Policy Statement, are allowed.
Description: This Funding Opportunity Announcement (FOA) invites applications seeking to develop novel, transformative artificial intelligence/machine learning (AI/ML) strategies, and computer automation, to integrate, extract, and interpret multi-omic (i.e., genome, epigenome, transcriptome, proteome, metabolome, microbiome, phenome) data sets from human exceptional longevity (EL) cohorts and multiple non-human species that display wide variation in life span and decipher the relationships between DNA, RNA, proteins, metabolites, and other cell variables, as well as links to disease risks and exceptionally healthy aging. The investigative team(s) for this FOA is/are expected to be multi-disciplinary, encompassing expertise in AI/ML and a variety of disciplines, including, but not limited to, aging biology, comparative biology, and bio/chemo informatics. This FOA utilizes the National Institutes of Health&apos;s Phased Innovation Award (R21/R33) funding mechanism. During the R21 phase, investigative teams will design and develop intelligent and innovative algorithms and novel AI/ML based computational strategies. During the R33 phase, teams will apply the developed AI/ML tools to complex, heterogenous multi-omic data sets from exceptional healthy aging human cohorts and non-human species to discover novel protective molecular factors that influence EL, and to develop translational strategies on omic based therapeutic target(s) to prevent, or delay, age-related diseases, including Alzheimers disease (AD) and AD-related dementia (ADRD), and enhance human health span.
GrantorContactEmailDescription: See Section VII. Agency Contacts within the full opportunity announcement for all other inquires.
GrantorContactText: NIH OER Webmaster &lt;br/&gt;OERWebmaster03@od.nih.gov