Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks

超大规模机器学习助力阿尔茨海默病生物库的发现

基本信息

批准号：
10475286
负责人：
Christos Davatzikos
金额：
$ 364.16万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-15 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10475286
关键词：
Academia Address Affect Algorithms Alzheimer disease detection Alzheimer disease prevention Alzheimer&apos s Disease Alzheimer&apos s disease patient Alzheimer&apos s disease related dementia Alzheimer&apos s disease risk Alzheimer&apos s disease therapeutic Alzheimer’s disease biomarker Amyloid Amyloid beta-Protein Artificial Intelligence Big Data Methods Biological Biological Markers Cerebrovascular Disorders Clinical Clinical Trials Cognition Cognitive Collection Communities Data Data Analyses Data Collection Data Set Data Sources Databases Disease Disease Progression Drug Industry Drug Targeting Early Diagnosis Educational workshop Frequencies Genes Genetic Genetic Diseases Genome Genomics Genotype Goals Image Impaired cognition Informatics International Lead Leadership Link Machine Learning Mathematics Methods Modality Molecular Multimodal Imaging Nerve Degeneration Network-based Neurobiology Participant Pattern Pharmaceutical Preparations Phenotype Predictive Value Research Research Personnel Research Priority Risk Role Sampling Scoring Method Source Speed Statistical Methods Structure Subgroup Systems Analysis Training United States National Institutes of Health Variant Work analytical method analytical tool artificial intelligence algorithm base biobank case control clinical subtypes cloud based cognitive performance cognitive system cognitive testing data curation data dissemination data harmonization data integration deep learning demographics design disorder subtype drug development drug discovery drug repurposing endophenotype experience genetic analysis genetic signature genetic variant genome sequencing genome wide association study genomic biomarker genomic data genomic predictors hazard high throughput analysis imaging biomarker imaging genetics improved indexing informatics tool interest large scale data machine learning method neurobiological mechanism neuroimaging neuroimaging marker neuropathology new therapeutic target next generation sequencing novel personalized medicine phenotypic data pre-clinical predictive marker predictive modeling predictive test prevent response risk prediction specific biomarkers tau Proteins tool translational impact whole genome

项目摘要

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.

抽象的响应 PAR-19-269“阿尔茨海默病遗传和表型数据的认知系统分析” （U01 临床试验不允许）”，我们的项目联合了 AD 基因组学、机器学习和人工智能领域的专家（包括深度学习）、大规模数据集成和国际数据协调，以精心设计的方式工作我们将与 NIH、ADSP 和 NIAGADS 密切合作，开发一套联盟结构。用于阿尔茨海默病 (AD) 基因组超大规模分析的补充大数据分析方法和表型数据现在由阿尔茨海默病测序项目产生。 (ADSP)、国家阿尔茨海默病协调中心 (NACC)、阿尔茨海默病神经影像倡议 (ADNI)、加速药物合作伙伴 AD (AMP-AD) 和英国生物银行 (UKBB) 远远超出了所有机构的容量当前的分析方法未能跟上如此大量的数据收集的规模和速度。遗传和表型数据的增长需要新的、更强大的算法来：（1）存储、管理和以不断扩大的规模操纵全基因组序列和相关数据（2）发现新的 AD 风险；通过合并信息学和 AD 基因组数据库来确定保护位点 (3) 将全基因组变化与现在定义生物 AD 的 ATN(v) 生物标志物。 - 将提供新的人工智能和深度学习工具来发现大规模基因组学数据中的特征 - 相关整体我们经验丰富的 PI 团队将通过合并所有相关数据源，将基因组数据转化为生物标记特征。协调美国各地的努力来创建这些大规模数据分析工具。我们的 MPI 团队和 6 个核心。领导者拥有数十年与 AD 社区一起开拓机器学习的经验 AD 遗传学和神经影像学方法，包括国际神经影像学联盟的领导地位专用核心专注于基因组、成像和认知数据协调。然后有效地导入人工智能方法和信息学管道中，从而进行 AD 研究社区利用来自 ADSP、NACC、ADNI 的超大规模、多维基因组和表型数据，我们的工作是由精心设计和协调的联盟组织的，并由所有人指导。 AD 基因组学和神经影像领域的利益相关者、临床领导者和先驱分析师。工具将推进 AD 基因组学研究，并将包括培训工作和专门的药物再利用核心。该团队的努力将加速对遗传、分子和神经生物学机制的理解。 AD 的研究，对疾病和药物开发产生重大转化影响。