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An Explainable Unified AI Strategy for Efficient and Robust Integrative Analysis of Multi-omics Data from Highly Heterogeneous Multiple Studies

Award Number: R33AG083299
ORGANIZATION: NATIONAL INSTITUTE ON AGING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/15/2023
PERIOD OF PERFORMANCE END DATE: 07/31/2028

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An Explainable Unified AI Strategy for Efficient and Robust Integrative Analysis of Multi-omics Data from Highly Heterogeneous Multiple Studies - Healthy centenarians carry protective variants that counteract age-related disease risk variants, the former of which are mostly rare. Therefore, markers associated with exceptional longevity (EL) need be discovered through integrative multi-omics data analysis to improve detection power. However, existing integrative analysis method for multi-omics data do not model the relationships among markers in a modality and among studies, muddying the efficient use of pertinent information provided by multi-omics data from heterogeneous studies. We propose a unified AI strategy that models the relationships among markers, modalities, and studies, and learns nonlinear low-dimensional representations of data in a common space via graph neural networks (GNN). We achieve deep integration by enforcing the maximization of similarities between study representations and the phenotype prediction accuracy in a single GNN. The proposal has three specific aims: 1) Develop an explainable unified AI strategy and software for efficient and robust integrative analysis of multi-omics data from highly heterogeneous multiple studies. 2) Apply the methods developed in Aim 1 to Long-Life Family Study (LLFS) and Integrative Longevity Omics (ILO) data provided by the EL consortium to identify EL-associated pathways and biomarkers. 3) Apply the methods developed in Aim 1 to omics data from human and 100 species of diverse lifespan provided by the EL consortium to identify conserved and species-specific EL-associated pathways and markers. The outcome of this work will result in a publicly available integrative omics data analysis software which not only is able to identify robust longevity-associated pathways and biomarkers, but will also be applicable to any complex disease study with similar omics data analysis demands. Our work will contribute significantly to identify therapeutic interventions for improving human health.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $1,892,433 )
2025	2025	JACKSON LABORATORY	600 MAIN ST	BAR HARBOR	ME	04609	HANCOCK	USA	Aging Research	000	2	8/4/2025	EXTENSION WITH OR WITHOUT FUNDS	$1,892,433
														Subtotal = $1,892,433

Grand Total All Awards = $1,892,433

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An Explainable Unified AI Strategy for Efficient and Robust Integrative Analysis of Multi-omics Data from Highly Heterogeneous Multiple Studies

Award Number: R33AG083299

ORGANIZATION: NATIONAL INSTITUTE ON AGING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 08/15/2023

PERIOD OF PERFORMANCE END DATE: 07/31/2028

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