Systems Immunology in Aging and Chronic Diseases of Aging

衰老和慢性衰老疾病中的系统免疫学

• 批准号: 10459457
• 负责人: Duygu Ucar
• 金额: $ 3.44万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10459457
• 关键词: Academia; Address; Affect; Age; Aging; Alzheimer' s Disease; Animal Model; Cardiovascular Diseases; Career Mobility; Cell physiology; Cells; Chronic Disease; Cities; Clinical; Collaborations; Complex; Computational Biology; Computational Technique; Computing Methodologies; Connecticut; Data; Data Analyses; Data Science; Development; Discipline; Disease; Economic Burden; Educational workshop; Elderly; Ethnic group; Etiology; Feedback; Female; Fostering; Functional disorder; Funding; Genomic medicine; Genomics; Geroscience; Goals; Health; Home; Human; Immune; Immune system; Immunologist; Immunology; Industry; Inflammation; Institution; Lead; Learning; Link; Location; Longevity; Machine Learning; Malignant Neoplasms; Metabolic; Methods; Molecular; Mus; Nature; Non-Insulin-Dependent Diabetes Mellitus; Organism; Outcome; Participant; Phenotype; Play; Population; Postdoctoral Fellow; Process; Public Health; Pythons; Race; Research; Research Personnel; Resources; Role; Scholarship; Science; Scientist; Series; Shock; Societies; Students; Support System; System; Techniques; Technology; The Jackson Laboratory; Time; Tissues; Training; Underrepresented Minority; Universities; Work; aging population; cancer type; career; clinical biomarkers; clinically significant; data complexity; data mining; deep learning algorithm; epigenomics; frailty; genomic biomarker; genomic data; graduate student; health economics; healthspan; innovation; interdisciplinary approach; interest; machine learning algorithm; machine learning model; meetings; next generation; novel strategies; posters; programs; recruit; response; senescence; single cell technology; skills; symposium; technology development; transcriptomics; translational approach

PROJECT SUMMARY / ABSTRACT The funds requested in this R13 application are for partial support of “Systems Immunology in Aging and Chronic Diseases of Aging” annual meetings to be offered each September from 2020 through 2022 at The Jackson Laboratory for Genomic Medicine (JAX-GM) in Farmington, Connecticut. This meeting builds on its very successful first instance in 2019 and will bring together up to 150 interdisciplinary scientists including molecular biologists, immunologists, computational biologists, and geriatricians, who share a common interest in understanding aging and aging-associated disease at the systems level. Many aging-associated diseases, such as cancer and cardiovascular disease, are influenced by dysfunctions in the immune system. Recent advances in genomic profiling techniques (e.g., single cell transcriptomics) provide an opportunity to uncover aging-related changes in human cells/tissues and to link these changes to health and lifespan. The wealth and complexity of data produced using these technologies is ever increasing, as is the need to develop advanced computational methods to mine and integrate these data. Despite this need, there are currently no formal venues at which scientists, specifically those in the aging field, can be trained in the basics and application of data mining techniques (i.e., machine learning algorithms). Furthermore, current conferences on aging are not aimed at specifically bringing together computational biologists, immunologists and basic and clinical aging researchers. Therefore, the objectives of this meeting are: (1) to recognize and emphasize the highly interdisciplinary nature of the aging field and to promote and accelerate collaborations and cross-pollination of ideas across the three disciplines: aging, immunology, and computational biology; (2) to provide trainees (students and postdoctoral fellows) an opportunity to closely interact with, and gain feedback from, more senior investigators to advance their projects and establish connections to help build their careers; and (3) to provide an opportunity for researchers in the field of aging to learn the basics of machine learning techniques, which they will be able to immediately apply to their own research upon return to their home institutions. We will reach these objectives through carrying out the following Aims. In Aim 1, we will organize an interdisciplinary meeting and hands-on workshop focused on aging and aging-related diseases. The meeting will include a 2-day seminar session featuring talks by leading scientists, followed by a 1-day hands-on workshop on the basics of machine learning. In Aim 2, we will promote interactions to foster collaborative research and career advancement, including through a poster session. In Aim 3, we will recruit diverse attendees. Our proposed speaker list features several female scientists, and we will use our partnership networks to specifically recruit attendees from nationally underrepresented racial and ethnic groups. The ultimate goal of the meeting is to advance the aging research field through expediting collaborations and the understanding of aging-related genomic data via application of advanced data mining approaches.

项目摘要 /摘要 此R13应用程序中要求的资金是为了部分支持“衰老和慢性的系统无限学 衰老的疾病“每年9月从2020年至2022年在杰克逊举行的年度会议 康涅狄格州法明顿的基因组医学实验室。 2019年成功的第一案例，最多将汇集150名跨学科科学家 生物学家，免疫学家，计算生物学家和老年医生都有共同的兴趣 了解系统水平的衰老和衰老相关疾病。 作为癌症和心血管疾病，免疫系统中的功能障碍会影响。 在基因组分析技术中（例如，单细胞转录组学）为发现与衰老有关的机会提供了机会 人类细胞/组织的变化，将变化与健康和寿命联系起来。 随着需要开发高级计算的需求，使用这些技术生产的数据一直在增加 尽管需要这种需求，但可以挖掘和整合这些数据 科学家，特别是在老龄化领域的科学家，可以在数据挖掘的基础和应用中接受培训 技术（即机器学习算法）。 专门将计算生物学家，免疫学和临床老龄化研究人员汇总在一起。 因此，这次会议的目标是：（1）认识并强调高度的跨学科性质 衰老领域的范围，并在三个三三个三个三个三个三个三个三个三个三个三个三个三个三个三个三个三个三个三个三个三个三个三三 学科：老化，免疫学和计算生物学；（2） 研究员）与更多高级调查员进行紧密互动并获得反馈的机会 他们的项目并建立联系以帮助建立职业；（3） 衰老领域的研究人员学习机器学习技术的基础知识 即将对他们的研究申请自己的研究。 通过在AIM 1中实现以下目标。 对与衰老有关的疾病的讲习班将包括为期2天的研讨会 由领先的科学家进行演讲，然后进行为期1天的动手讲习班，讲述机器学习的基础知识。 在AIM 2中，我们将促进互动以促进协作研究和职业发展，包括throoth 海报会议。 科学家，我们将利用用户专门招募来自国家的参与者。 规定种族和族裔的最终目标是对衰老研究的最终目标 通过加快合作和对衰老相关的基因组的理解，通过应用程序关闭 高级数据挖掘方法。