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.

Public Health Relevance

Our Ultrascale Machine Learning Initiative, or ?ULTRA? - is a coordinated national initiative to develop transformative AI approaches for high throughput analysis of next generation sequencing (NGS) and related AD biomarker and cognitive data. Biomarker data related to AD are being collected at ?ultra-scale? and are likely to unlock numerous opportunities for AD treatment, yet the rapid collection of such data far exceed our current capacity to analyze it. Our collective effort in this proposal will sieve extensive genomic, biomarker, and cognitive data to extract and prioritize the features that are essential to address fundamental barriers to AD prevention and drug discovery.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZAG1)
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Miller, Marilyn
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University of Southern California
Schools of Medicine
Los Angeles
United States
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