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).

摘要 确定异常长寿（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，但可能无法推断生理上的重要原因。机械地理解如何 单个分子组分在系统中一起工作，以及系统如何受到影响和适应 分子变化需要了解从DNA到 从RNA到蛋白质到代谢物再到有机体的表型。通过整合多组学数据， 多模态机器学习和多层网络模型有望解决这一缺陷。然而，在这方面， 现有的机器学习方法受到高维性、非均匀性、大量 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大量的初步成果， 在阿尔茨海默病药物发现和使用C.线虫病 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 克服一些障碍，以发现EL的分子机制。创新和 验证新算法，以1）通过去除环境和生物来协调不均匀的数据集 混杂因素（例如，年龄、物种等）和技术偏见（例如，批量效应），2）显式建模 生物信息从DNA流到RNA流到蛋白质流到代谢物流到生物体表型流，以及3） 确定导致EL的遗传因素和分子相互作用。具体而言，我们将：（1）发展 MuLGIT，一个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 迁移学习方法PATH-AE用于跨物种组学数据集成和建模， 进化上保守的和物种特异性的EL分子决定簇;（3）确定分子靶点， 通过合并多组学数据集成的新方法， 化学基因组学和扰动基因组学的最新方法;以及（4）实验验证计算 使用C. elegans模型该项目的完成将使我们能够识别新的生物标志物， 药物靶点和与显著寿命（EL）相关的药理学试剂。