AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer's disease via deep learning

AIM-AI：通过深度学习绘制阿尔茨海默病的可操作、集成和多尺度遗传图谱

• 批准号: 10668829
• 负责人: Christopher A. Gaiteri
• 金额: $ 127.81万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10668829
• 关键词: Accountability; Address; Aging; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease risk; Artificial Intelligence; Autopsy; Awareness; Biological Markers; Brain; Brain region; Catalogs; Cells; Characteristics; Chromatin; Chromosome Mapping; Clinical; Clinical Data; Clinical Sciences; Communities; Complex; DNA Methylation; DNA Sequence; Data; Data Analyses; Disease; Disease Progression; Etiology; Gene Expression; Genetic; Genetic Diseases; Genetic Risk; Genetic Transformation; Genomics; Goals; Histone Acetylation; Image; Impaired cognition; Individual; Informatics; Intercept; Joints; Light; Link; Machine Learning; Mediating; Medical Imaging; Methods; MicroRNAs; Modality; Modeling; Molecular; Molecular Biology; Multiomic Data; Nucleic Acid Regulatory Sequences; Ontology; Pathology; Pharmaceutical Preparations; Phenotype; Play; Population; Process; Proteins; Proteome; Regulation; Research; Research Personnel; Resolution; Resources; Role; Signal Transduction; System; Systems Analysis; Systems Biology; Time; Tissues; Training; Translational Research; Validation; Variant; brain tissue; cell type; cognitive system; computational neuroscience; convolutional neural network; data curation; deep learning; deep learning model; design; disease diagnosis; disease phenotype; disorder risk; drug development; drug discovery; epigenomics; feature extraction; functional genomics; genetic analysis; genetic architecture; genetic association; genetic variant; genomic data; imaging genetics; knowledgebase; machine learning method; multimodal data; multiple omics; neuroimaging; novel; phenotypic data; polygenic risk score; protein expression; response; risk variant; single-cell RNA sequencing; tool

Project Summary In response to PAR-19-269 “Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data”, in this proposal we assemble an interdisciplinary team to develop novel and robust analytical approaches to effectively address the current challenges in capitalizing on genetics, omics and neuroimaging data in Alzheimer’s disease (AD). Our team expertise covers complex disease genetics, functional genomics and regulation, machine learning/deep learning, systems-oriented research, neuroimaging, drug informatics, computational neuroscience, and clinical and translational science. Artificial intelligence (AI) has been shown powerful in uncovering hidden features that are critical to disease diagnosis or etiology. However, merely making the AI models “explainable” does nothing for explainability of AD, including major effects detailed in molecular biology, pathology, and neuroimaging. Our overall goal is to develop and implement a robust AI framework, namely AIM-AI, for transforming the genetic catalog of AD in a way that is Actionable, Integrated and Multiscale, so that genetic factors have clear utility for subsequent etiological studies. To make our findings Actionable, we explore multiple-omics systems that functionally intercept the effects of genetic factors at the cell-type-specific and single-cell resolution. We will develop Integrated and brain-data-driven collective systems, covering genetic, phenotypic, multi-omics, cell context, neuroimaging and knowledgebase information. Finally, a Multiscale systems biology approach will be implemented to identify genetic, neuroimaging, and phenotypic changes, which in combination can better explain the genetic architecture of AD and its cognitive decline. We will mine the AD characteristics at functional, cellular, tissue- and cell type-specific, and neuroimaging levels, enabling more rigorous assessment and validation that genetics effects indeed play out in cognitive decline and AD phenotypes. Our proposal has three specific aims. Aim 1: Develop a deep learning framework, “DeepBrain-AD”, to characterize the genetic risk of AD using both bulk brain tissue and single-cell regulatory genomics. Aim 2. Identify variants that account for cognitive decline due to AD progression by developing deep learning models that connect multiple modalities (imaging, clinical, genomics) in a joint analysis framework. Aim 3. Assess and validate the genetic variants from Aims 1 and 2 using multiple omics data to illustrate molecular systems which mediate their effects. In summary, we will uniquely investigate and validate genetic variants and other markers in AD at multi-omics level, at the cell-type context and single-cell resolution; and link the genetic association signals with functional regulation, protein expression, and neuroimaging context; and finally explain their roles in cognitive decline due to AD progression. The successful completion of this project will generate a robust AIM-AI framework, including machine learning methods/tools, resources, and scientific discoveries through integrative omics, deep learning, and other systems-based approaches, which will be immediately shared with AD and other disease research communities.

项目摘要 针对PAR-19-269“阿尔茨海默病遗传和表型数据的认知系统分析”， 在本提案中，我们组建了一个跨学科团队，以开发新颖而强大分析方法， 有效地解决当前利用遗传学、组学和神经成像数据的挑战， 阿尔茨海默病（AD）。我们的团队专业知识涵盖复杂的疾病遗传学，功能基因组学和 监管，机器学习/深度学习，面向系统的研究，神经成像，药物信息学， 计算神经科学，临床和转化科学。人工智能（AI）已经被证明 在发现对疾病诊断或病因学至关重要的隐藏特征方面非常强大。然而，仅仅使 “可解释”的AI模型对AD的可解释性没有任何作用，包括分子中详细描述的主要效应。 生物学病理学和神经影像学我们的总体目标是开发和实施一个强大的AI框架， 即AIM-AI，用于以可操作的，集成的和 多尺度，使遗传因素有明确的效用，为随后的病因学研究。使我们的 研究结果可行，我们探索多组学系统，功能拦截遗传因素的影响， 细胞类型特异性和单细胞分辨率。我们将开发集成和大脑数据驱动的集体 系统，涵盖遗传，表型，多组学，细胞背景，神经成像和知识库信息。 最后，将实施多尺度系统生物学方法，以确定遗传，神经成像， 表型变化，这两者结合起来可以更好地解释AD的遗传结构及其认知功能 下降我们将从功能、细胞、组织和细胞类型特异性以及 神经成像水平，使更严格的评估和验证，遗传效应确实发挥了作用， 认知能力下降和AD表型。我们的建议有三个具体目标。目标1：发展深度学习 框架，“DeepBrain-AD”，使用大块脑组织和单细胞来表征AD的遗传风险 调控基因组学目标2.通过以下方法确定导致AD进展导致认知下降的变异： 开发深度学习模型，在联合分析中连接多种模式（成像、临床、基因组学） 框架.目标3.使用多组学数据评估和验证目标1和2的遗传变异， 示出了介导其作用的分子系统。总之，我们将独特地调查和验证 在多组学水平、细胞类型背景和单细胞分辨率下AD中的遗传变异和其他标志物; 并将遗传关联信号与功能调节、蛋白质表达和神经影像背景联系起来; 最后解释它们在AD进展引起的认知下降中的作用。这个项目的顺利完成 将产生一个强大的AIM-AI框架，包括机器学习方法/工具，资源和科学 通过综合组学、深度学习和其他基于系统的方法， 立即与AD和其他疾病研究团体分享。