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: 10907083
• 负责人: Jason H. Moore
• 金额: $ 41.06万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10907083
• 关键词: Affect; Algorithms; Alzheimer' s Disease; Alzheimer’s disease biomarker; American; Artificial Intelligence; Biochemical Pathway; Biological; Biological Markers; Biomedical Computing; Brain imaging; Clinical; Collection; Computer software; Data; Data Analyses; Data Sources; Dimensions; Disease; Ensure; FDA approved; Genetic; Genomics; Goals; Heart; Image; Informatics; Investments; Joints; Knowledge; Learning; Machine Learning; Methods; Modeling; Pattern; Pharmaceutical Preparations; Population Study; Public Health Informatics; Reproducibility; Research; Research Project Grants; Source; Structure; Technology; artificial intelligence algorithm; artificial intelligence method; biobank; biomedical informatics; clinical decision support; cost; drug development; feature selection; innovation; large scale data; machine learning algorithm; machine learning model; multimodality; multiple omics; novel; novel therapeutics; open source; programs; protein aggregation; protein misfolding; user-friendly

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.

阿尔茨海默氏病（AD）是一种常见疾病，部分是由于蛋白质错误折叠和 聚合。广告研究是国家优先事项，每年有550万美国人受到影响 费用超过2500亿美元，无法治愈。这是对 在实验和人群研究中收集潜水员临床和生物学数据。 人工智能（AI）和机器学习有可能揭示临床和 多源大规模阿尔茨海默氏症的数据尚未使用标准方法找到。 我们在这里提出了一个全面的生物医学计算和健康信息研究项目 开发和应用最先进的AI算法和生物医学软件，以分析大型 扩展广告数据。这项建议的信息计划的核心是Pennai方法， 可以通过AI算法自动化机器学习的软件，该算法可以从先验中学习 分析。这种方法使猜测摘除了正确的机器学习算法 和参数设置，从而使每个人都可以访问此计算技术。 具体而言，我们将开发三种新颖的信息方法，以量身定制pennai以分析 广告数据。首先，我们将开发一个多模式相互作用（M2I）特征选择算法 识别预测AD的遗传相互作用（AIM 1）。第二，我们将开发一个 知识驱动的多派集成（KMI）算法，用于结合AI的OMICS功能 AD分析（AIM 2）。第三，我们将开发多维大脑成像幻象（MBIO） 用于预测AD的多源大规模数据联合分析的集成框架。 最后，我们将将所有三种生物医学信息的方法整合到我们的开源pennai中 软件包并将其应用于两项基于人群的大型AD研究。我们期望佩奈 将揭示广告的新生物标志物，这些广告将为更好的治疗和临床决定打开大门 支持。

项目成果

Omics Methods in Toxins Research-A Toolkit to Drive the Future of Scientific Inquiry.

• DOI: 10.3390/toxins14110761
• 发表时间: 2022-11-04
• 期刊: Toxins
• 影响因子: 4.2
• 作者: Romano JD