# Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks

> **NIH NIH U01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2020 · $3,767,729

## Abstract

ABSTRACT
In response to PAR-19-269 “Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data
(U01 Clinical Trial Not Allowed)”, our project unites experts in AD genomics, machine learning and AI (including
deep learning), large-scale data integration, and international data harmonization to work in a carefully-designed
Consortium Structure in close partnership with the NIH, ADSP, and NIAGADS. We will develop a suite of
complementary big data analytic approaches for ultra-scale analysis of Alzheimer’s Disease (AD) genomic
and phenotypic data. The vast data volumes now generated by the Alzheimer’s Disease Sequencing Project
(ADSP), National Alzheimer’s Coordinating Center (NACC), Alzheimer’s Disease Neuroimaging Initiative (ADNI),
Accelerating Medications Partnership AD (AMP-AD), and UK Biobank (UKBB), far exceed the capacity of all
current analytic methods, which have not kept pace with the scale and speed of data collection. This vast amount
of genetic and phenotypic data mandates new and more powerful algorithms to: (1) store, manage, and
manipulate whole-genome sequences and associated data on an ever-growing scale; (2) discover novel AD risk
and protective loci by merging informatics and AD genomics databases; (3) relate whole-genome changes to
the ATN(v) biomarkers that now define biological AD. Our Ultrascale Machine Learning Initiative, or “ULTRA”
- will offer new AI and deep learning tools to discover features in massive scale genomics data - relating whole
genome data to biomarker features by merging all relevant data sources. Our team of experienced PIs will
coordinate efforts across the U.S. to create these large-scale data analytic tools. Our MPI team and 6 Core
Leads have decades of experience working together and with the AD community in pioneering machine learning
methods for AD genetics and neuroimaging, including leadership of international neuroimaging consortia across
the world. Dedicated Cores focus on Genomic, Imaging, and Cognitive Data Harmonization. Curated data will
then be efficiently imported into AI approaches and informatics pipelines that will allow the AD research
community to leverage ultra-scale, multidimensional genomic and phenotypic data from the ADSP, NACC, ADNI,
AMP-AD, and others. Our work is organized by a carefully-designed and coordinated Consortium guided by all
stake-holders, clinical leaders, and pioneering analysts in AD genomics and neuroimaging. Our ultrascale AI
tools will advance AD genomics research and will include efforts in training, and a dedicated Drug Repurposing
Core. This team effort will accelerate understanding of the genetic, molecular and neurobiological mechanisms
of AD, yielding significant translational impact on disease and drug development.

## Key facts

- **NIH application ID:** 10028746
- **Project number:** 1U01AG068057-01
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Christos Davatzikos
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $3,767,729
- **Award type:** 1
- **Project period:** 2020-09-15 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10028746

## Citation

> US National Institutes of Health, RePORTER application 10028746, Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks (1U01AG068057-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10028746. Licensed CC0.

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