# Artificial Intelligence Strategies for Alzheimer's Disease Research

> **NIH NIH U01** · CEDARS-SINAI MEDICAL CENTER · 2023 · $1,592,634

## Abstract

Alzheimer's disease (AD) is a common disease that is partly due to protein misfolding and
aggregation. Research on AD is a national priority with 5.5 million Americans affected at an annual
cost of more than $250 billion and no available cure. This is despite heavy investments in the
collection of diverse clinical and biological data in experimental and population-based studies.
Artificial intelligence (AI) and machine learning have the potential to reveal patterns in clinical and
multi-source large-scale Alzheimer’s data that have not been found using standard approaches.
We propose here a comprehensive biomedical computing and health informatics research project
to develop and apply cutting-edge AI algorithms and biomedical software for the analysis of large-
scale AD data. At the heart of this proposed informatics program is the PennAI method and
software for automating machine learning through an AI algorithm that can learn from prior
analyses. This approach takes the guesswork out of picking the right machine learning algorithms
and parameter settings thus making this computing technology accessible to everyone.
Specifically, we will develop three novel informatics methods to tailor PennAI to the analysis of
AD data. First, we will develop a Multi-Modal Interaction (M2I) feature selection algorithm for
identifying genetic interactions that are predictive of AD (AIM 1). Second, we will develop a
Knowledge-driven Multi-omics Integration (KMI) algorithm for combining omics features for AI
analysis of AD (AIM 2). Third, we will develop a Multidimensional Brain Imaging Omics (MBIO)
integration framework for the joint analysis of multi-source large-scale data for predicting AD.
Finally, we will integrate all three biomedical informatics methods into our open-source PennAI
software package and apply it to two large population-based studies of AD. We expect PennAI
will reveal new biomarkers for AD that will open the door for better treatments and clinical decision
support.

## Key facts

- **NIH application ID:** 10691474
- **Project number:** 5U01AG066833-04
- **Recipient organization:** CEDARS-SINAI MEDICAL CENTER
- **Principal Investigator:** Jason H. Moore
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $1,592,634
- **Award type:** 5
- **Project period:** 2021-09-30 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10691474

## Citation

> US National Institutes of Health, RePORTER application 10691474, Artificial Intelligence Strategies for Alzheimer's Disease Research (5U01AG066833-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10691474. Licensed CC0.

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