AI models of multi-omic data integration for ming longevity core signaling pathways

长寿核心信号通路多组学数据整合的人工智能模型

• 批准号: 10745189
• 负责人: Fuhai Li
• 金额: $ 46.07万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10745189
• 关键词: Affect; Age; Age of Onset; Aging; Alzheimer' s Disease; Artificial Intelligence; Autophagocytosis; Biological Process; Brain; Cardiovascular Diseases; Centenarian; Chronic; Chronic Kidney Failure; Cirrhosis; Collaborations; Communities; Complex; Complex Genetic Trait; Data; Data Analyses; Data Set; Development; Diabetes Mellitus; Disease; Disease Management; Environment; Environmental Risk Factor; Estrogens; Ethnic Origin; FOXO3A gene; Fasting; Gastroenteritis; Genetic; Genome; Genomics; Goals; Head; Health; Healthcare; Heart Diseases; Individual; Inflammaging; Inflammation; Insulin Resistance; Kidney Failure; Knowledge; Life Style; Liver Fibrosis; Longevity; Malignant Neoplasms; Mediation; Methylation; Modeling; Multiomic Data; Names; Organ; Pharmaceutical Preparations; Pharmacogenomics; Phase; Phenotype; Process; Proteins; Proteome; Proteomics; Province; Pulmonary Fibrosis; Reproducibility; Request for Applications; Risk; Risk Factors; Signal Pathway; Signal Transduction; Stomach; Stress; Toes; Visual; X Chromosome; age related; analysis pipeline; artificial neural network; cohort; data integration; deep learning; disorder prevention; disorder risk; epigenome; experience; graph neural network; healthspan; improved; interest; knowledge graph; large scale data; machine learning model; metabolome; metabolomics; microbiome; multiple datasets; multiple omics; new therapeutic target; novel; open source; phenome; prevent; protective factors; response; sex; social factors; therapeutic target; tool; transcriptome; transcriptomics

PROJECT SUMMARY Exceptional longevity (EL) is strongly correlated with exceptional health span, lower risk and delayed onset of age-related diseases. Moreover, EL is a complex genetic trait, like aging-related diseases, affected by polygenic targets, and other factors, like sex, ethnicity, lifestyle choices, social and environmental factors. Thus, in EL studies, single protective genetic targets usually have weaker effects upon survival to extreme age. Whereas, the right combination of genetic targets, as well as other factors, can have a stronger effect. Therefore, it is important to discover these protective factors, genetic targets and subsequent signaling pathways of EL, which are the critical basis to guide the development of novel medications and management for disease prevention/treatment to extend health and life span. Large-scale and multi-omics datasets, like genome, epigenome, transcriptome, proteome, metabolome, microbiome, phenome, of large-scale cohorts of centenarians and exceptional long-lived individuals, have been being generated in multiple EL projects. Whereas, it remains challenging to integrate and interpret complex multi-omics datasets. In response to the NIH RFA-AG-23-033, we propose to improve and develop novel artificial intelligence (AI) models that can efficiently integrate and interpret the EL multi-omics datasets, and identify risk and protective targets and medications to correct the disease risk signaling pathways for disease prevention and long and healthy life span extension. Deep learning (DL) and AI models have been widely used in the healthcare field and outperform traditional machine learning models, and thus offering solutions to this critical problem. We have rich experience in developing interpretable AI models of multi-omics data analysis for target ranking and core signaling network inference. In this study, we will (Aim 1) develop two (GNN) AI models, PathFormer and PathFinder, for unbiased core signaling pathways inference using multi-omics data (unbiased/unguided inference); (aim 2): develop a novel GNN AI model, modular k-Hop DeepNetFlow, for hypothesis guided core signaling pathway inference using multi-omics data (semi-guided inference); (Aim 4): develop novel DeepDrugMap knowledge graph, and Knowledge-driven, Multi-Module, Multi-Evidence (M3E) models to predict drugs that can boost protective signaling and inhibit the risk signaling pathways for disease prevention/treatment; develop a novel, open-source visual programming tool, LongevityOmicNet, to support the dissemination and reproducible analysis of the AI models with diverse supportive datasets, to the broader EL or aging study community. Also (Aim 3): collaborating with Dr. Michael Province (Co-PI), leading the LLFS project in WashU, we will apply these AI models to identify EL-associated protective factors, like the Sex, Genetics, Insulin resistance, Environment factors (SGIE-factors), and associated signaling pathways/biological processes, using large-scale multi-omics data of EL studies, i.e., LLFS, LG and ILO studies.

项目摘要 特殊的寿命（EL）与特殊的健康跨度密切相关，风险较低和延迟发作 与年龄有关的疾病。此外，EL是一种复杂的遗传特征，例如与衰老有关的疾病，受到衰老相关疾病的影响 多基因目标以及其他因素，例如性别，种族，生活方式选择，社会和环境因素。 因此，在EL研究中，单个保护性遗传靶标通常对生存到极端年龄的影响较弱。 而遗传靶标的正确组合以及其他因素可以具有更强的作用。 因此，重要的是要发现这些保护因素，遗传靶标和随后的信号传导 EL的途径，这是指导新药物和管理发展的关键基础 预防疾病/治疗以延长健康和寿命。大规模和多摩变数据集，例如 基因组，表观基因组，转录组，蛋白质组，代谢组，微生物组，现象，来自大规模的队列 一百岁的人和杰出的长寿个人已经在多个EL项目中产生。 鉴于，整合和解释复杂的多摩变数据集仍然具有挑战性。 为了响应NIH RFA-AG-23-033，我们建议改善和发展新颖的人工智能（AI） 可以有效地集成和解释EL Multi-Omics数据集并确定风险和保护性的模型 目标和药物以纠正预防疾病的疾病风险信号传导途径 健康的寿命延长。深度学习（DL）和AI模型已在医疗保健领域广泛使用 超过传统的机器学习模型，从而为这个关键问题提供解决方案。我们 在为目标排名和 核心信号网络推断。在这项研究中，我们将（AIM 1）开发两个（GNN）AI模型，Pathformer和 探路者，用于使用多摩学数据的公正核心信号通路推理（无偏/非引导 推理）; （AIM 2）：开发一种新型的GNN AI模型，模块化K-HOP DeepNetflow，用于假设引导的核心 使用多摩学数据（半引导推断）推理信号通路推理； （目标4）：发展小说 DeepDrugMap知识图和知识驱动的多模块，多通用（M3E）模型 预测可以提高保护性信号并抑制疾病的风险信号传导途径的药物 预防/治疗；开发一种新颖的开源视觉编程工具LongevityOmicNet，以支持 具有不同支持数据集的AI模型的传播和可重现分析，以更广泛的 EL或老化研究社区。另外（AIM 3）：与Michael Province（Co-Pi）合作，领导LLFS 在Washu的项目，我们将应用这些AI模型来识别EL相关的保护因素，例如性别， 遗传学，胰岛素抵抗，环境因素（SGIE因子）和相关的信号通路/生物学 使用EL研究的大规模多摩学数据，即LLFS，LG和ILO研究。