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; 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 为了响应19 pars-pars，“阿尔茨海默氏病遗传和表型数据的认知系统分析”， 在此提案中，我们组装了一个跨学科团队，以开发出新颖而强大的分析方法 有效地解决当前资本化遗传学，OMIC和神经影像数据的挑战 阿尔茨海默氏病（AD）。我们的团队专业知识涵盖了复杂的疾病遗传学，功能基因组学和 调节，机器学习/深度学习，面向系统的研究，神经影像学，药物信息， 计算神经科学以及临床和转化科学。已显示人工智能（AI） 有力地揭示了对疾病诊断或病因至关重要的隐藏特征。但是，只是制作 AI模型“可解释”对AD的解释无济于事，包括分子中详细介绍的主要影响 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强大的AI框架， 即AIM-AI，用于以可操作，集成和 多尺度，以便遗传因素具有清晰的效用，以便随后的病因研究。让我们的 发现可在功能上拦截遗传因素的影响的多摩学系统探索可行的发现。 在细胞型特异性和单细胞分辨率下。我们将开发综合和大脑驱动的集体 系统，涵盖遗传，表型，多词，细胞环境，神经影像学和知识基础信息。 最后，将采用多尺度系统生物学方法来识别遗传，神经影像和 表型变化，结合可以更好地解释AD及其认知的遗传结构 衰退。我们将在功能，细胞，组织和细胞类型特异性上挖掘AD特征，并且 神经影像学水平，实现更严格的评估和验证，遗传学的影响确实在 认知能力下降和AD表型。我们的建议具有三个具体目标。目标1：发展深度学习 框架，“深脑 - AD”，以表征使用散装脑组织和单细胞的AD的遗传风险 调节基因组学。目标2。确定因广告进展而导致认知能力下降的变体 在联合分析中开发连接多种方式（成像，临床，基因组学）的深度学习模型 框架。目标3。使用多个OMIC数据来评估和验证目标1和2的遗传变异 插图的分子系统，介导了它们的作用。总而言之，我们将唯一调查和验证 在多词级别，在细胞类型的环境和单细胞分辨率下，AD中的遗传变异和其他标记； 并将遗传关联信号与功能调节，蛋白质表达和神经成像环境联系起来； 并最终解释了由于广告进展而导致的认知下降中的作用。该项目的成功完成 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 通过集成的OMIC，深度学习和其他基于系统的方法的发现，这将是 立即与AD和其他疾病研究社区分享。