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Artificial Intelligence Strategies for Alzheimer's Disease Research

Artificial Intelligence Strategies for Alzheimer's Disease Research
阿尔茨海默病研究的人工智能策略
批准号:
10582512
负责人:
Jason H. Moore
金额:
$160.94万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-09-30 至 2026-08-31

项目摘要

项目成果

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中文摘要
翻译
阿尔茨海默病(AD)是一种常见的疾病,部分原因是蛋白质错误折叠和
英文摘要
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.
期刊论文(7)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1002/psp4.12975
发表时间: 2023-08
期刊: CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY
影响因子: 3.5
作者: [Hao, Yun, Romano, Joseph D. D., Moore, Jason H. H.]
通讯作者: Moore, Jason H. H.
DOI: 10.1016/j.patter.2022.100565
发表时间: 2022-09-09
期刊: PATTERNS
影响因子: 6.5
作者: [Hao, Yun, Romano, Joseph D., Moore, Jason H.]
通讯作者: Moore, Jason H.
DOI: 10.1021/acs.chemrestox.2c00074
发表时间: 2022-08-15
期刊: CHEMICAL RESEARCH IN TOXICOLOGY
影响因子: 4.1
作者: [Romano, Joseph D., Hao, Yun, Moore, Jason H., Penning, Trevor M.]
通讯作者: Penning, Trevor M.
DOI: 10.1142/9789811286421_0008
发表时间: 2023-12
期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
影响因子: --
作者: [Jason H Moore;Xi Li;Jui-Hsuan Chang;Nicholas P. Tatonetti;Dan Theodorescu;Yong Chen;F. Asselbergs;Mythreye Venkatesan;Zhiping Wang]
通讯作者: Jason H Moore;Xi Li;Jui-Hsuan Chang;Nicholas P. Tatonetti;Dan Theodorescu;Yong Chen;F. Asselbergs;Mythreye Venkatesan;Zhiping Wang
共 6 条
    Bioinformatics Strategies for Genome Wide Association Studies
    • 批准号:
      10616262
    • 项目类别:
    • 资助金额:
      $36.95万
    • 财政年份:
      2022
    • 负责人:
      Jason H. Moore
    • 依托单位:
    Bioinformatics Strategies for Genome Wide Association Studies
    • 批准号:
      10654872
    • 项目类别:
    • 资助金额:
      $34.89万
    • 财政年份:
      2022
    • 负责人:
      Jason H. Moore
    • 依托单位:
    Admin-Core
    • 批准号:
      10685537
    • 项目类别:
    • 资助金额:
      $48.11万
    • 财政年份:
      2021
    • 负责人:
      Jason H. Moore
    • 依托单位:
    Artificial Intelligence Strategies for Alzheimer's Disease Research
    • 批准号:
      10491672
    • 项目类别:
    • 资助金额:
      $159.26万
    • 财政年份:
      2021
    • 负责人:
      Jason H. Moore
    • 依托单位:
    海外基金