# IDEAL-XAI: Advancing Explainable AI to Identify Early Driver Events of Alzheimer's Disease

> **NIH NIH RF1** · UNIVERSITY OF WASHINGTON · 2024 · $1,761,543

## Abstract

Project Summary
Alzheimer’s disease (AD) lacks effective treatment, primarily due to our limited scientific understanding of the
early cellular pathways leading to end-stage pathologies like amyloid-β (Aβ) and tau. To bridge this knowledge
gap, we propose significant advancements in XAI that can expedite data-driven discoveries in AD.
 The growing availability of multimodal single-cell data from donor cohorts, covering the entire spectrum of
AD pathology, underscores the need for machine learning (ML) models to learn low-dimensional embeddings,
a crucial step in interpreting these datasets. Current XAI technology addresses the opacity of supervised ML
models by calculating feature attributions, indicating the importance of individual features like gene expression
levels to the model’s output, such as a Aβ level, for specific samples such as cells. However, a significant gap
exists between model explanations and biological insights: (1) Current XAI techniques face limitations in the
context of unsupervised embedding learning and the integration of prior knowledge. (2) The computation of
accurate feature attributions poses challenges due to its exponential complexity. (3) Validating hypothesized
causal factors requires interventional experiment.
 IDEAL-XAI addresses these limitations by focusing on the following objectives:
Aim 1: Generate biologically informed explanations of AD progression. To bridge the gap between gene-level
attributions and systems-level explanations, our innovative XAI methods operate within a unified latent
embedding space, incorporating diverse biological concepts from prior knowledge. These methods interpret
patterns within the embedding space, including disease progression, to identify potential driver genes.
Aim 2: Compute accurate feature attributions to identify putative AD drivers. We propose theoretically
grounded techniques to rigorously compute Shapley values for modern large deep models such as transformers
and graph neural networks, handle feature correlations and multimodality, and evaluate feature attribution
methods in a principled manner to assist investigators discern the most effective techniques for their applications.
Aim 3: Validate computational hypotheses in human neurons and microglia. To validate our computational
hypotheses regarding AD drivers, we will perform experiments in human neurons and microglia, two critical
cell types in AD, across various stages. By modulating the expression of potential drivers in these cells, we aim
to measure their impact on various AD-related phenotypes, providing essential insights into therapeutic targets.
 The successful IDEAL-XAI project will significantly advance XAI principles and techniques, with broader
applications in biomedical research areas. Moreover, it will enhance our understanding of AD and expedite the
discovery of potential therapeutic targets. IDEAL-XAI represents a crucial step toward unraveling the mysteries
of AD and translating complex ML models...

## Key facts

- **NIH application ID:** 10942708
- **Project number:** 1RF1AG088824-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Su-In Lee
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,761,543
- **Award type:** 1
- **Project period:** 2024-08-15 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10942708

## Citation

> US National Institutes of Health, RePORTER application 10942708, IDEAL-XAI: Advancing Explainable AI to Identify Early Driver Events of Alzheimer's Disease (1RF1AG088824-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10942708. Licensed CC0.

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