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

基本信息

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
项目类别：
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&apos 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

项目摘要

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.

项目概要超长寿命 (EL) 与超常健康寿命、较低风险和延迟发病密切相关与年龄有关的疾病。此外，EL 是一种复杂的遗传特征，就像与衰老相关的疾病一样，受以下因素影响：多基因目标以及其他因素，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，单一的保护性遗传目标通常对极端年龄的生存影响较弱。然而，遗传目标以及其他因素的正确组合可以产生更强的效果。因此，发现这些保护因素、遗传靶标和后续信号传导非常重要 EL途径，是指导新药开发和管理的重要基础用于疾病预防/治疗以延长健康和寿命。大规模和多组学数据集，例如大规模群体的基因组、表观基因组、转录组、蛋白质组、代谢组、微生物组、表型组多个 EL 项目不断涌现出百岁老人和杰出的长寿人士。然而，整合和解释复杂的多组学数据集仍然具有挑战性。为了响应 NIH RFA-AG-23-033，我们建议改进和开发新型人工智能 (AI) 可以有效集成和解释 EL 多组学数据集并识别风险和保护的模型纠正疾病风险信号通路的目标和药物，以预防疾病和长期治疗健康寿命延长。深度学习（DL）和AI模型已广泛应用于医疗保健领域并超越传统的机器学习模型，从而为这一关键问题提供解决方案。我们在开发用于目标排序的多组学数据分析的可解释人工智能模型方面拥有丰富的经验核心信令网络推理。在本研究中，我们将（目标 1）开发两种（GNN）AI 模型：PathFormer 和 PathFinder，用于使用多组学数据进行无偏核心信号通路推断（无偏/无引导推理）; （目标 2）：开发一种新颖的 GNN AI 模型，模块化 k-Hop DeepNetFlow，用于假设引导核心使用多组学数据进行信号通路推断（半引导推断）；（目标 4）：开发小说 DeepDrugMap 知识图谱和知识驱动、多模块、多证据 (M3E) 模型预测可以增强保护性信号传导并抑制疾病风险信号传导途径的药物预防/治疗；开发一种新颖的开源可视化编程工具 LongevityOmicNet，以支持具有不同支持数据集的人工智能模型的传播和可重复分析，以更广泛的方式 EL 或老龄化研究社区。另外（目标 3）：与 Michael Province 博士（Co-PI）合作，领导 LLFS 在华盛顿大学的项目中，我们将应用这些人工智能模型来识别与 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 因素）和相关信号传导途径/生物过程，使用 EL 研究的大规模多组学数据，即 LLFS、LG 和 ILO 研究。