The Integrated Stress Response in Human Islets During Early T1D

早期 T1D 期间人体胰岛的综合应激反应

• 批准号: 10592566
• 负责人: Thomas O Metz
• 金额: $ 40.13万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10592566
• 关键词: Address; Affect; Area; Artificial Intelligence; Autoimmunity; Beta Cell; Biological; Biological Markers; Cell Survival; Cell physiology; Cellular Stress; Clinical; Collaborations; Communities; Community Networks; Computer software; Data; Data Set; Defect; Development; Development Plans; Experimental Designs; Feedback; Generations; Grant; Human; Immunologics; Individual; Inflammation; Ingestion; Insulin-Dependent Diabetes Mellitus; Knowledge; Lead; Learning; Machine Learning; Measurement; Messenger RNA; Metadata; Methods; Modeling; Molecular; Molecular Profiling; Monitor; Multiomic Data; Ontology; Pathogenesis; Pathway interactions; Physics; Production; Proteins; Proteomics; PubMed; Publications; Publishing; Readiness; Research; Research Design; Resources; Risk; Sample Size; Science; Signal Pathway; Standardization; Stress; Technology; TensorFlow; Testing; Traction; Translating; base; biological adaptation to stress; biomarker discovery; biomarker panel; biomarker validation; classical conditioning; computerized data processing; data repository; feature extraction; heterogenous data; improved; insulin dependent diabetes mellitus onset; islet; learning community; lipidomics; machine learning method; machine learning model; minimally invasive; multidisciplinary; multiple omics; neoantigens; novel; parent grant; parent project; phrases; predictive modeling; rapid growth; repository; response; software development; stress granule; trend

ABSTRACT The project, Integrated Stress Response in Human Islets During Early Type 1 Diabetes (T1D), hypothesizes that the activation of the integrated stress response and formation of stress granules is an early cellular response initiating β cell stress in T1D that determines cell survival and can be monitored in pre- and early-T1D individuals with minimal invasiveness. A multidisciplinary Team science approach is being taken to test this hypothesis, collecting a large suite of heterogenous data, such as mRNA, lipidomics, proteomics and immunologic measurements. Machine learning is being used to extract a multi-biomarker panel to aid in stratifying stress in human islets and translating these findings to individuals at-risk for T1D and new-onset T1D. Although we are formatting the multi-omics data for this specific machine learning task within the parent grant, the data being generated, as well as our data collected from prior collaborations, are not generally AI/ML-ready for general application of methods. They are however excellent candidates to be used as “flagship” datasets for AI/ML readiness, both to test novel AI/ML approaches to tackle data pre-processing challenges and to extract molecular signatures of T1D. These two gaps in analyses are the central themes of two aims. The first aim focuses on the generation of AI/ML ready omics datasets that are properly annotated to address challenges in sparsity and bias, such as imputation and batch correction. The second aim focuses AI/ML ready multi-omic datasets to enable new studies in using machine learning to elicit biomarkers and pathway-level molecular signatures from the data focused on standard AI/ML methods, as well as those specialized for small sample size. Dataset machine learning model cards will be utilized to better enable to AI/ML research communities to utilize these datasets in an efficient manner. For both aims there is a key focus on generating reusable software approaches to generate data packages that can be directly imported into the most common AI/ML packages and released to the AI/ML community through a variety of resources that enable feedback to continually improve and refine the AI/ML readiness software development plan.

抽象的 该项目是早期1型糖尿病（T1D）期间人类胰岛中的综合应力反应，假设 综合应力反应的激活和应力颗粒的形成是早期的细胞反应 在T1D中启动β细胞应激，该β细胞应激决定细胞存活，可以在前和早期T1D个体中进行监测 具有最小的侵入性。正在采用多学科团队科学方法来检验这一假设， 收集大量的异源数据，例如mRNA，脂肪组学，蛋白质组学和免疫学 测量。机器学习被用于提取多生物标志物面板，以帮助对压力进行分层 人类胰岛并将这些发现转化为T1D和新发行T1D的人。虽然我们是 在父母赠款中格式化此特定机器学习任务的多摩斯数据，数据为 生成的以及我们从先前协作中收集的数据通常不准备使用AI/ML 方法的应用。但是，他们是AI/ML的“旗舰”数据集的出色候选人 准备就绪，都可以测试新型AI/ML方法，以应对预处理挑战的数据 T1D的签名。分析中的这两个差距是两个目标的中心主题。第一个目的侧重于 生成AI/ML准备就绪的OMICS数据集，这些数据集经过适当注释以应对稀疏和偏见的挑战， 例如插补和批处理。第二个目标重点是AI/ML准备就绪的多OMIC数据集以启用 从数据 专注于标准的AI/ML方法，以及专门用于小样本量的方法。数据集机 学习模型卡将用于更好地启用AI/ML研究社区 有效的方式。对于这两个目标，都有关键的重点是生成可重复使用的软件方法来生成 可以直接导入最常见的AI/ML软件包并释放到AI/ML的数据软件包 通过各种资源社区，使反馈能够不断改善和完善AI/ML 准备软件开发计划。