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

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos s disease model Alzheimer&apos 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

项目摘要

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.

项目概要针对 PAR-19-269“阿尔茨海默病遗传和表型数据的认知系统分析”，在这项提案中，我们组建了一个跨学科团队来开发新颖且强大的分析方法有效解决当前利用遗传学、组学和神经影像数据的挑战我们团队的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、神经影像学、药物信息学、计算神经科学、临床和转化科学已经被证明。强大的功能可以揭示对疾病诊断或病因学至关重要的隐藏特征。 AI模型“可解释”对AD的可解释性没有任何帮助，包括分子中详细描述的主要影响我们的总体目标是开发和实施一个强大的人工智能框架，即 AIM-AI，用于以可操作、集成和可操作的方式改变 AD 的遗传目录多尺度，使遗传因素对后续的病因学研究具有明确的效用。可行的发现，我们探索在功能上拦截遗传因素影响的多组学系统我们将开发集成的、脑数据驱动的集体。系统，涵盖遗传、表型、多组学、细胞背景、神经影像和知识库信息。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，结合起来可以更好地解释 AD 的遗传结构及其认知我们将挖掘功能、细胞、组织和细胞类型特异性的 AD 特征，以及神经影像水平，从而能够更严格地评估和验证遗传效应确实在我们的提案有三个具体目标：开发深度学习。框架“DeepBrain-AD”，利用大块脑组织和单细胞来表征 AD 的遗传风险目标 2. 识别导致 AD 进展导致认知能力下降的变异。开发在联合分析中连接多种模式（成像、临床、基因组学）的深度学习模型目标 3. 使用多个组学数据评估和验证目标 1 和 2 的遗传变异说明介导其作用的分子系统总之，我们将进行独特的研究和验证。 AD 中多组学水平、细胞类型背景和单细胞分辨率的遗传变异和其他标记；并将遗传关联信号与功能调节、蛋白质表达和神经影像背景联系起来；最后，他们解释了 AD 进展导致的认知能力下降的作用。将生成一个强大的 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的发现，这将是立即与 AD 和其他疾病研究团体分享。