Transformative Artificial Intelligence and Machine Learning Based Strategies to Identify Determinants of Exceptional Health and Life Span (R21/R33 Clinical Trial Not Allowed) | RFA AG 22 022

Opportunity Information: Apply for RFA AG 22 022

The NIH grant opportunity "Transformative Artificial Intelligence and Machine Learning Based Strategies to Identify Determinants of Exceptional Health and Life Span (R21/R33 Clinical Trial Not Allowed)" (RFA-AG-22-022) supports the creation of new, high-impact AI and machine learning methods designed to make sense of complex biological data tied to exceptional longevity and healthy aging. The central goal is not to fund traditional wet-lab discovery or clinical intervention studies, but to accelerate the development of computational approaches that can integrate and interpret massive, heterogeneous multi-omic datasets. In practical terms, applicants are expected to build transformative AI/ML strategies and automation pipelines that can connect different layers of biology, such as genome, epigenome, transcriptome, proteome, metabolome, microbiome, and phenome, and then extract biologically meaningful patterns that help explain why some humans achieve exceptional longevity with relatively good health, and why certain animal species live dramatically longer than others.

A key emphasis of this FOA is integrative, cross-domain analysis that links molecular and cellular variables to outcomes relevant to aging. Projects should aim to decipher relationships among DNA, RNA, proteins, metabolites, and other cellular measurements, and then relate those patterns to disease risk and resilience, with a particular focus on exceptionally healthy aging rather than aging plus heavy disease burden. The FOA explicitly encourages work that leverages both human exceptional longevity cohorts and comparative biology across multiple non-human species with wide variation in lifespan, since cross-species signals can help distinguish deep, conserved mechanisms of lifespan and healthspan from signals that are population- or environment-specific. This makes the opportunity especially suited to proposals that can harmonize or model differences in data structure, measurement platforms, and biological context, while still producing interpretable insights.

The funding mechanism is the NIH Phased Innovation Award (R21/R33), which is structured to support a two-stage progression. The R21 phase is generally meant for early, exploratory development and proof-of-concept work, such as designing new algorithms, building automated workflows, demonstrating feasibility on representative datasets, and defining performance metrics. The R33 phase supports expansion and more robust development once predefined milestones are met, such as scaling methods to larger datasets, improving generalizability across cohorts and species, strengthening validation and benchmarking, and delivering usable software, models, or analytic frameworks. Because the FOA is labeled "Clinical Trial Not Allowed," the proposed work must not include clinical trial activities as defined by NIH; the focus is on computational method development and data-driven inference rather than testing interventions in humans.

Eligibility is broad and includes many types of U.S.-based organizations and governmental entities. Eligible applicants include state, county, and local governments; special district governments; independent school districts; public and state-controlled institutions of higher education; private institutions of higher education; federally recognized Native American tribal governments; tribal organizations that are not federally recognized; public housing authorities and Indian housing authorities; nonprofits with or without 501(c)(3) status (other than institutions of higher education); for-profit organizations other than small businesses; small businesses; and other organizations that meet NIH eligibility rules. The announcement also calls out additional eligible applicant categories such as Historically Black Colleges and Universities (HBCUs), Hispanic-serving Institutions, Tribally Controlled Colleges and Universities (TCCUs), Alaska Native and Native Hawaiian Serving Institutions, and Asian American Native American Pacific Islander Serving Institutions (AANAPISIs), along with faith-based or community-based organizations, eligible federal agencies, regional organizations, and U.S. territories or possessions.

Restrictions related to foreign involvement are specific. Non-domestic (non-U.S.) entities and non-U.S. foreign institutions are not eligible to apply as the primary applicant organization. However, non-domestic components of U.S. organizations may participate, and foreign components (as NIH defines them in the NIH Grants Policy Statement) are allowed. In other words, the lead applicant must be an eligible domestic entity, but collaborations that include foreign components can be part of the overall project when justified and compliant with NIH policy.

Administrative details from the opportunity listing indicate this is a discretionary NIH grant in the health category (CFDA 93.866), with an original closing date of October 28, 2021, and a creation date of July 6, 2021. The listing does not provide an award ceiling or expected number of awards in the excerpt provided, so applicants would typically need to consult the full FOA text for budget limits, project period guidance, review criteria, and the specific milestone expectations tied to transition from the R21 to the R33 phase.

Overall, the opportunity is aimed at teams that can push beyond standard bioinformatics by delivering genuinely new AI/ML approaches and automated systems that can handle the scale and complexity of multi-omics, reconcile differences across cohorts and species, and produce interpretable outputs tied to mechanisms of disease resistance, exceptional health, and extended lifespan. The best fit proposals are likely to be those that combine strong machine learning innovation with careful biological grounding, rigorous benchmarking, and clear plans for translating complex multi-omic signals into insights that aging researchers can use.

• The National Institutes of Health in the health sector is offering a public funding opportunity titled "Transformative Artificial Intelligence and Machine Learning Based Strategies to Identify Determinants of Exceptional Health and Life Span (R21/R33 Clinical Trial Not Allowed)" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 93.866.
• This funding opportunity was created on 2021-07-06.
• Applicants must submit their applications by 2021-10-28. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
• Eligible applicants include: State governments, County governments, City or township governments, Special district governments, Independent school districts, Public and State controlled institutions of higher education, Native American tribal governments (Federally recognized), Public housing authorities/Indian housing authorities, Native American tribal organizations (other than Federally recognized tribal governments), Nonprofits having a 501 (c) (3) status with the IRS, other than institutions of higher education, Nonprofits that do not have a 501 (c) (3) status with the IRS, other than institutions of higher education, Private institutions of higher education, For-profit organizations other than small businesses, Small businesses, Others.

Apply for RFA AG 22 022

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