AI-powered cross-level cross-species omics data integration to elucidate mechanisms of EL

人工智能驱动的跨级别跨物种组学数据集成阐明 EL 机制

• 批准号: 10729946
• 负责人: Alicia Melendez
• 金额: $ 45.85万
• 依托单位: HUNTER COLLEGE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10729946
• 关键词: Address; Affect; Age; Aging; Algorithms; Alzheimer' s Disease; Binding; Biological; Biological Markers; Biological Models; Biology; Caenorhabditis elegans; Chemicals; Collaborations; DNA; Data; Data Analyses; Data Reporting; Data Set; Dimensions; Disease; Drug Modelings; Drug usage; Foundations; Genetic; Genomics; Genotype; Health; Human; Individual; Knowledge; Laboratories; Learning; Longevity; Machine Learning; Mathematics; Methodology; Methods; Modeling; Molecular; Molecular Target; Multiomic Data; Nature; Pathway interactions; Performance; Pharmacologic Substance; Pharmacology; Phase; Phenotype; Physiological; Proteins; Proteomics; RNA; Reproducibility; Resources; System; Work; biological systems; biomarker identification; cancer genomics; data harmonization; data integration; data mining; data modeling; data resource; deep learning; diverse data; drug discovery; druggable target; epigenomics; feature selection; genetic information; genome-wide; genomic data; heterogenous data; high dimensionality; high throughput technology; improved; in vivo Model; innovation; learning strategy; machine learning algorithm; machine learning model; metabolomics; model organism; mouse model; multilevel analysis; multimodality; multiple omics; network models; new therapeutic target; novel; novel marker; pharmacologic; phenomics; predictive modeling; screening; success; targeted agent; trait; transcriptomics; transfer learning; transmission process

Abstract It is a formidable task to identify the molecular causes of complicated traits such as exceptional longevity (EL). The majority of machine learning algorithms generate mathematical correlations between genotypes and phenotypes, but may fail to infer physiologically significant causes. A mechanistic understanding of how individual molecular components work together in a system and how the system is affected and adapted to the molecular change requires knowledge of molecular interactions across all biological levels, from DNAs to RNAs to proteins to metabolites to organismal phenotypes. By integrating multi-omics data, recent approaches in multi-modal machine learning and multi-layer network model promise to address this deficiency. However, existing machine learning approaches are hampered by high-dimensionality, non-uniformity, numerous confounders, and biological differences in multi-omics data across data resources, data domains, and species as well as lack of interpretability due to the black-box nature of machine learning models. We will develop a transformative deep learning framework to address challenges for multi-omics data integration and predictive modeling of causal genotype-EL associations. This project is established on our substantial preliminary results, successes in systems pharmacology for Alzheimer's disease drug discovery and using C. elegans as disease and aging models, and close collaborations between experimental and computational laboratories. We shall overcome several obstacles in order to discover the molecular mechanisms of EL. We will develop and validate novel algorithms to 1) harmonize non-uniform data sets by removing environmental and biological confounding factors (e.g., age, species, etc.) and technical biases (e.g., batch effect), 2) explicitly model the biological information flow from DNAs to RNAs to proteins to metabolites to organismal phenotypes, and 3) determine causal genetic factors and molecular interactions underlying EL. Specifically, we will: (1) develop MuLGIT, a causal deep learning-powered cross-layer multi-omics harmonization and integration framework that follows the central dogma of biology for deciphering the molecular interplays underlying EL; (2) develop a transfer learning method PATH-AE for cross-species omics data integration and modeling for elucidating evolutionarily conserved and species-specific molecular determinants of EL; (3) identify molecular targets and pharmaceutical agents of EL by merging new methodologies for multi-omics data integration with state-of-the- art methods for chemical genomics and perturbation genomics; and (4) experimentally validate computational predictions using C. elegans models. Completion of this project will allow us to identify novel biomarkers, druggable targets, and pharmacological agents associated with remarkable lifespan (EL).

抽象的 确定超长寿命（EL）等复杂性状的分子原因是一项艰巨的任务。 大多数机器学习算法生成基因型和基因型之间的数学相关性 表型，但可能无法推断出生理上重要的原因。机械地理解如何 单个分子成分在系统中协同工作，以及系统如何受到影响和适应 分子变化需要了解所有生物水平上的分子相互作用，从 DNA 到 RNA、蛋白质、代谢物、生物体表型。通过整合多组学数据，最新的方法 多模态机器学习和多层网络模型有望解决这一缺陷。然而， 现有的机器学习方法受到高维性、非均匀性、数量众多的阻碍 跨数据资源、数据域和物种的多组学数据中的混杂因素和生物学差异 以及由于机器学习模型的黑盒性质而缺乏可解释性。我们将开发一个 变革性的深度学习框架，以应对多组学数据集成和预测的挑战 因果基因型-EL 关联的建模。这个项目是建立在我们实质性的初步成果的基础上的， 在阿尔茨海默病药物发现和使用秀丽隐杆线虫作为疾病的系统药理学方面取得成功 和老化模型，以及实验和计算实验室之间的密切合作。我们将 为了发现 EL 的分子机制，克服了一些障碍。我们将开发和 验证新算法以 1）通过消除环境和生物来协调非均匀数据集 混杂因素（例如年龄、物种等）和技术偏差（例如批次效应），2）明确建模 生物信息从 DNA 流向 RNA，再到蛋白质，再到代谢物，再到生物体表型，3) 确定 EL 的致病遗传因素和分子相互作用。具体来说，我们将：（一）开发 MuLGIT，因果深度学习驱动的跨层多组学协调和集成框架 遵循生物学的中心法则来破译 EL 背后的分子相互作用； (2) 开发一个 用于跨物种组学数据集成和建模的迁移学习方法 PATH-AE EL 的进化保守性和物种特异性分子决定因素； (3) 识别分子靶标 通过将多组学数据集成的新方法与最新技术相结合，开发 EL 药物制剂 化学基因组学和微扰基因组学的艺术方法； (4) 实验验证计算 使用秀丽隐杆线虫模型进行预测。该项目的完成将使我们能够识别新的生物标志物， 可药物靶标以及与显着寿命（EL）相关的药物。