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HF-ETIOLOGY: Heart Failure Endotypes from Ethical, Multi-Modal AI driven and Molecular/Phenotypic Data Integration Enabled Discovery

Award Number: OT2OD038083
ORGANIZATION: OFFICE OF THE DIRECTOR, NIH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: OTHER
PERIOD OF PERFORMANCE START DATE: 09/01/2025
PERIOD OF PERFORMANCE END DATE: 08/31/2026

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HF-ETIOLOGY: Heart Failure Endotypes from Ethical, Multi-Modal AI driven and Molecular/Phenotypic Data Integration Enabled Discovery - Abstract Precision medicine is rapidly evolving through integrating multi-modal data to tailor treatments and deepen our understanding of diseases like heart failure (HF). Our initiative, HF-ETIOLOGY develops an ethical multi-modal AI framework that harnesses the power of multi-modal data—phenotypic, multi-omic, and socio-behavioral—to identify distinct HF endotypes. Our approach is distinguished by its co-design and iterative development of novel multi-modal AI models for disease endotyping, including advanced Bayesian generative tensor models and temporal tensor models and network medicine approaches. These methods allow us to integrate diverse data modalities and structures, such as longitudinal clinical data and complex multi-omic networks, seamlessly with behavioral and Social Determinants of Health (SDoH) factors. Our project centers around four main objectives: 1) Establish a FAIR-CARE framework to co-design HF- ETIOLOGY data and models; 2) Co-design Generative and Adjustable Prior Bayesian Tensor Factorization (GAP-BTF) to integrate behavioral and SDoH factors with multi-omic network feature learning to identify HF endotypes; 3) Co-design temporal non-negative tensor factorization model (TNTF) to integrate longitudinal phenotypic data while jointly modeling behavioral and SDoH factors for HF endotyping; 4) Identify likely risk genes/targets and pathways and investigate drug repurposing by incorporating SDoH factors for clinically relevant HF endotypes with MAI and network medicine co-design. Central to our methodology is a co-design framework that involves continuous engagement with stakeholders. This collaborative approach ensures that the development of our AI models is informed by clinical insights and aligned with ethical standards, thereby enhancing the practicality and relevance of our research in the clinical setting.


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 = $1,699,258 )
2025	2025	NORTHWESTERN UNIVERSITY	633 CLARK ST	EVANSTON	IL	60208	COOK	USA	Trans-NIH Research Support	000	1	8/22/2025	NEW	$1,699,258
														Subtotal = $1,699,258

Grand Total All Awards = $1,699,258

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HF-ETIOLOGY: Heart Failure Endotypes from Ethical, Multi-Modal AI driven and Molecular/Phenotypic Data Integration Enabled Discovery

Award Number: OT2OD038083

ORGANIZATION: OFFICE OF THE DIRECTOR, NIH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: OTHER

PERIOD OF PERFORMANCE START DATE: 09/01/2025

PERIOD OF PERFORMANCE END DATE: 08/31/2026

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