An Explainable Unified AI Strategy for Efficient and Robust Integrative Analysis of Multi-omics Data from Highly Heterogeneous Multiple Studies

一种可解释的统一人工智能策略，用于对来自高度异质性多项研究的多组学数据进行高效、稳健的综合分析

• 批准号: 10729965
• 负责人: Gregory W Carter
• 金额: $ 55.2万
• 依托单位: JACKSON LABORATORY
• 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/10729965
• 关键词: Address; Age; Aging; Algorithms; Biological Markers; Blindness; Centenarian; Cognitive aging; Complex; Computer software; Data; Data Analyses; Data Reporting; Data Set; Dementia; Deposition; Detection; Dimensions; Disease; Equilibrium; Family Study; Funding Opportunities; Gene Expression; Genetic; Health; Heterogeneity; Human; Individual; Knowledge; Learning; Life; Longevity; Methods; Modality; Modeling; Multiomic Data; Mus; Orthologous Gene; Outcome; Pathway interactions; Phase; Phenotype; Population; Sampling; Structure; Testing; Therapeutic Intervention; Training; Work; age related; computerized tools; data integration; differential expression; disorder risk; graph neural network; high dimensionality; human data; human old age (65+); improved; interest; mouse model; offspring; programs; protective allele; prototype; response; risk variant; supervised learning; tool

Healthy centenarians carry protective variants that counteract age-related disease risk variants, the former of which are mostly rare. Therefore, markers associated with exceptional longevity (EL) need be discovered through integrative multi-omics data analysis to improve detection power. However, existing integrative analysis method for multi-omics data do not model the relationships among markers in a modality and among studies, muddying the efficient use of pertinent information provided by multi-omics data from heterogeneous studies. We propose a unified AI strategy that models the relationships among markers, modalities, and studies, and learns nonlinear low-dimensional representations of data in a common space via graph neural networks (GNN). We achieve deep integration by enforcing the maximization of similarities between study representations and the phenotype prediction accuracy in a single GNN. The proposal has three specific aims: 1) Develop an explainable unified AI strategy and software for efficient and robust integrative analysis of multi-omics data from highly heterogeneous multiple studies. 2) Apply the methods developed in Aim 1 to Long-Life Family Study (LLFS) and Integrative Longevity Omics (ILO) data provided by the EL consortium to identify EL-associated pathways and biomarkers. 3) Apply the methods developed in Aim 1 to omics data from human and 100 species of diverse lifespan provided by the EL consortium to identify conserved and species-specific EL-associated pathways and markers. The outcome of this work will result in a publicly available integrative omics data analysis software which not only is able to identify robust longevity-associated pathways and biomarkers, but will also be applicable to any complex disease study with similar omics data analysis demands. Our work will contribute significantly to identify therapeutic interventions for improving human health.

健康的百岁老人携带保护性变异，可以抵消与年龄相关的疾病风险变异，前者大多数是罕见的。因此，需要通过整合多组学数据分析来发现与异常长寿（EL）相关的标记，以提高检测能力。然而，现有的多组学数据的综合分析方法没有建模的模式和研究之间的标记物之间的关系，混淆了有效利用的相关信息提供的多组学数据从异质性研究。我们提出了一个统一的人工智能策略，该策略对标记、模态和研究之间的关系进行建模，并通过图神经网络（GNN）在公共空间中学习数据的非线性低维表示。我们通过在单个GNN中执行研究表示和表型预测准确性之间的相似性最大化来实现深度集成。该提案有三个具体目标：1）开发一种可解释的统一AI策略和软件，用于对来自高度异质性多项研究的多组学数据进行有效和强大的综合分析。2)将目标1中开发的方法应用于EL联盟提供的长寿家族研究（LLFS）和综合长寿组学（ILO）数据，以确定EL相关途径和生物标志物。3)将目标1中开发的方法应用于EL联盟提供的人类和100个不同寿命物种的组学数据，以识别保守的和物种特异性的EL相关途径和标记。这项工作的成果将导致一个公开可用的综合组学数据分析软件，不仅能够识别强大的长寿相关途径和生物标志物，而且还将适用于具有类似组学数据分析需求的任何复杂疾病研究。我们的工作将大大有助于确定改善人类健康的治疗干预措施。