# AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer's disease via deep learning

> **NIH NIH U01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2024 · $1,291,066

## Abstract

Project Summary
In response to PAR-19-269 “Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data”,
in this proposal we assemble an interdisciplinary team to develop novel and robust analytical approaches to
effectively address the current challenges in capitalizing on genetics, omics and neuroimaging data in
Alzheimer’s disease (AD). Our team expertise covers complex disease genetics, functional genomics and
regulation, machine learning/deep learning, systems-oriented research, neuroimaging, drug informatics,
computational neuroscience, and clinical and translational science. Artificial intelligence (AI) has been shown
powerful in uncovering hidden features that are critical to disease diagnosis or etiology. However, merely making
the AI models “explainable” does nothing for explainability of AD, including major effects detailed in molecular
biology, pathology, and neuroimaging. Our overall goal is to develop and implement a robust AI framework,
namely AIM-AI, for transforming the genetic catalog of AD in a way that is Actionable, Integrated and
Multiscale, so that genetic factors have clear utility for subsequent etiological studies. To make our
findings Actionable, we explore multiple-omics systems that functionally intercept the effects of genetic factors
at the cell-type-specific and single-cell resolution. We will develop Integrated and brain-data-driven collective
systems, covering genetic, phenotypic, multi-omics, cell context, neuroimaging and knowledgebase information.
Finally, a Multiscale systems biology approach will be implemented to identify genetic, neuroimaging, and
phenotypic changes, which in combination can better explain the genetic architecture of AD and its cognitive
decline. We will mine the AD characteristics at functional, cellular, tissue- and cell type-specific, and
neuroimaging levels, enabling more rigorous assessment and validation that genetics effects indeed play out in
cognitive decline and AD phenotypes. Our proposal has three specific aims. Aim 1: Develop a deep learning
framework, “DeepBrain-AD”, to characterize the genetic risk of AD using both bulk brain tissue and single-cell
regulatory genomics. Aim 2. Identify variants that account for cognitive decline due to AD progression by
developing deep learning models that connect multiple modalities (imaging, clinical, genomics) in a joint analysis
framework. Aim 3. Assess and validate the genetic variants from Aims 1 and 2 using multiple omics data to
illustrate molecular systems which mediate their effects. In summary, we will uniquely investigate and validate
genetic variants and other markers in AD at multi-omics level, at the cell-type context and single-cell resolution;
and link the genetic association signals with functional regulation, protein expression, and neuroimaging context;
and finally explain their roles in cognitive decline due to AD progression. The successful completion of this project
will generate a robust AIM-AI framew...

## Key facts

- **NIH application ID:** 10927217
- **Project number:** 5U01AG079847-02
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Christopher A. Gaiteri
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,291,066
- **Award type:** 5
- **Project period:** 2023-09-15 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10927217

## Citation

> US National Institutes of Health, RePORTER application 10927217, AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer's disease via deep learning (5U01AG079847-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10927217. Licensed CC0.

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