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: 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/10582512
• 关键词: Artificial; Intelligence; Strategies; Alzheimer; Disease

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.

阿尔茨海默病 (AD) 是一种常见疾病，部分原因是蛋白质错误折叠和 AD 研究是国家优先事项，每年有 550 万美国人受到影响。 尽管投入巨资，但仍无法解决这一问题，成本已超过 2500 亿美元。 在实验和基于人群的研究中收集各种临床和生物学数据。 人工智能 (AI) 和机器学习有潜力揭示临床和医学领域的模式 尚未使用标准方法发现的多源大规模阿尔茨海默病数据。 我们在这里提出一个全面的生物医学计算和健康信息学研究项目 开发和应用尖端人工智能算法和生物医学软件来分析大型 该提议的信息学计划的核心是 PennAI 方法和 通过人工智能算法实现机器学习自动化的软件，该算法可以从先前的经验中学习 这种方法消除了选择正确的机器学习算法时的猜测。 和参数设置，从而使每个人都可以使用这种计算技术。 具体来说，我们将开发三种新颖的信息学方法，使 PennAI 能够分析 首先，我们将为 AD 数据开发多模态交互（M2I）特征选择算法。 确定可预测 AD 的遗传相互作用（AIM 1）。 知识驱动的多组学集成 (KMI) 算法，用于组合 AI 的组学特征 AD 分析（AIM 2）第三，我们将开发多维脑成像组学（MIO）。 用于联合分析多源大规模数据以预测 AD 的集成框架。 最后，我们将把所有三种生物医学信息学方法集成到我们的开源 PennAI 中 我们期望 PennAI 能够将其应用于两项基于人群的大型 AD 研究。 将揭示 AD 的新生物标志物，为更好的治疗和临床决策打开大门 支持。

项目成果

Cross-phenotype associations between Alzheimer's Disease and its comorbidities may provide clues to progression.

阿尔茨海默病及其合并症之间的交叉表型关联可能提供进展线索。

• 发表时间: 2023-11-07
• 作者: Moore, Anni;Ritchie, Marylyn D
• 通讯作者: Ritchie, Marylyn D

Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers.

用于机器学习分类器综合评估的生成性和可重复性基准。

• 发表时间: 2022-11-25
• 影响因子: 13.6
• 作者: Orzechowski, Patryk;Moore, Jason H
• 通讯作者: Moore, Jason H

SynTwin: A graph-based approach for predicting clinical outcomes using digital twins derived from synthetic patients.

SynTwin：一种基于图形的方法，使用来自合成患者的数字双胞胎来预测临床结果。

• 发表时间: 2024
• 作者: Moore, Jason H;Li, Xi;Chang, Jui;Tatonetti, Nicholas P;Theodorescu, Dan;Chen, Yong;Asselbergs, Folkert W;Venkatesan, Mythreye;Wang, Zhiping Paul
• 通讯作者: Wang, Zhiping Paul

Automating Predictive Toxicology Using ComptoxAI.

使用 ComptoxAI 自动化预测毒理学。

• 发表时间: 2022-08-15
• 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

Knowledge-guided deep learning models of drug toxicity improve interpretation.

知识引导的药物毒性深度学习模型改善了解释。

• 发表时间: 2022-09-09
• 作者: Hao, Yun;Romano, Joseph D;Moore, Jason H
• 通讯作者: Moore, Jason H