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 确定复杂性状的分子原因，例如特殊寿命（EL）是一项艰巨的任务。 大多数机器学习算法都会在基因型和 表型，但可能无法推断出生理上重要的原因。对如何的机械理解 单个分子组件在系统中共同起作用，以及系统如何影响并适应 分子变化需要了解从DNA到所有生物学水平的分子相互作用的知识 RNA至蛋白质到代谢物与有机表型。通过整合多摩斯数据，最近的方法 在多模式机器学习和多层网络模型中，有望解决此缺陷。然而， 现有的机器学习方法受到高维，不均匀性的阻碍 跨数据资源，数据域和物种的混杂因素以及多摩学数据的生物学差异 以及由于机器学习模型的黑盒性质而缺乏可解释性。我们将发展一个 转化深度学习框架以应对多摩斯数据集成和预测性的挑战 因果基因型 - EL关联的建模。该项目是根据我们的实质性初步结果建立的 阿尔茨海默氏病药物的系统药理学的成功，并将秀丽隐杆线虫用作疾病 和老化模型，以及实验实验室和计算实验室之间的密切合作。我们将 克服几个障碍物，以发现EL的分子机制。我们将发展和 验证新型算法至1）通过去除环境和生物学来统一不均匀数据集 混杂因素（例如，年龄，物种等）和技术偏见（例如批处理效应），2）明确对 从DNA到RNA再到蛋白质再到代谢物再到有机表型的生物信息流动，3） 确定EL的因果遗传因素和分子相互作用。具体来说，我们将：（1）开发 Mulgit，一种因果深度学习驱动的跨层多摩学的统一和集成框架 这是在生物学的中心教条，以破译EL的分子相互作用； （2）开发 传输学习方法的跨物种的传输方法OMICS数据集成和建模用于阐明 EL的进化保守和物种特异性的分子决定因素； （3）确定分子靶标和 EL的药物通过将新方法与多摩学数据整合与最先进的方法合并 化学基因组学和扰动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确定新颖的生物标志物， 可药物靶标和与显着寿命相关的药理剂（EL）。