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

项目总结 超乎寻常的长寿(EL)与超乎寻常的健康寿命、较低的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，就像与衰老有关的疾病一样，受到 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，单一的保护性基因靶点通常对极端年龄的存活率影响较小。 然而，基因靶标的正确组合以及其他因素可能会产生更强的影响。 因此，发现这些保护因子、遗传靶点和随后的信号转导非常重要。 EL途径，是指导新药开发和管理的关键基础 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，如 基因组，表观基因组，转录组，蛋白质组，代谢组，微生物组，表观基因组，表观基因组，转录组，蛋白质组 在多个EL项目中产生了百岁老人和特别长寿的人。 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，我们提出了改进和发展新型人工智能(AI)的建议 可以有效地集成和解释EL多组学数据集的模型，并识别风险和保护 纠正疾病风险信号通路的靶点和药物用于疾病预防和长期的 健康延寿。深度学习和人工智能模型已广泛应用于医疗保健领域 并超越了传统的机器学习模型，从而为这个关键问题提供了解决方案。我们 在开发多组学数据分析的可解释人工智能模型方面具有丰富的经验，用于目标排名和 核心信令网推论。在这项研究中，我们将(目标1)开发两个(GNN)人工智能模型，PathFormer和 路径查找器，使用多组学数据(无偏见/无指导)进行无偏见的核心信号通路推断 推理)；(目标2)：为假设指导的核心开发一个新的GNN人工智能模型，模块化k-Hop DeepNetFlow 利用多组学数据进行信号通路推理(半引导推理)；(目标4)：开发新的 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和抑制疾病风险信号通路的药物 预防/治疗；开发一个新的、开源的可视化编程工具LongevityOmicNet，以支持 将具有不同支持性数据集的人工智能模型的传播和可重复性分析推广到更广泛的领域 EL或老龄化研究社区。还(目标3)：与迈克尔省博士合作(共同派)，领导LLFS 项目，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，如性， 遗传学、胰岛素抵抗、环境因子(SGIE因子)及相关信号通路/生物学 进程，使用大规模多组学研究的数据，即LLFS、LG和劳工组织的研究。

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.