Systems Biology of Aging: Data-science meets Gero-science

衰老系统生物学：数据科学与老年科学的结合

• 批准号: 9912317
• 负责人: Duygu Ucar
• 金额: $ 3.86万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-27 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9912317
• 关键词: Academia; Address; Affect; Aging; Alzheimer' s Disease; Animal Model; Biology of Aging; Cardiovascular Diseases; Career Mobility; Cell physiology; Cells; Cities; Clinical; Collaborations; Complex; Computational Biology; Computational Technique; Computing Methodologies; Connecticut; Data; Data Analyses; Data Science; Data Scientist; Development; Discipline; Disease; Economic Burden; Educational workshop; Elderly; Ethnic group; Etiology; Feedback; Female; Fostering; Functional disorder; Funding; Genomic medicine; Genomics; Geroscience; Goals; Health; Home environment; Human; Immune; Immune system; Immunologist; Immunology; Impaired cognition; Industry; Institution; Lead; Learning; Link; Location; Longevity; Machine Learning; Malignant Neoplasms; Methods; Modeling; 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; Systems Biology; Techniques; Technology; The Jackson Laboratory; Time; Tissues; Training; Underrepresented Minority; Universities; Work; aging population; cancer type; career; clinical biomarkers; clinically significant; data mining; epigenome; epigenomics; genomic biomarker; genomic data; genomic profiles; graduate student; health economics; healthspan; innovation; interdisciplinary approach; interest; machine learning algorithm; meetings; next generation; novel strategies; posters; programs; recruit; response; senescence; skills; symposium; technology development; transcriptome; transcriptomics; translational approach

PROJECT SUMMARY / ABSTRACT The funds requested in this R13 application are for partial support of “Systems Biology of Aging: Data-science meets Gero-science” annual meetings to be offered each August/September from 2019 through 2022 at The Jackson Laboratory for Genomic Medicine (JAX-GM) in Farmington, Connecticut. This meeting 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 本R13申请中所要求的资金用于部分资助《老龄化的系统生物学：数据科学》 将于2019年至2022年每年8/9月在 位于康涅狄格州法明顿的杰克逊基因组医学实验室(JAX-GM)。这次会议将带来 多达150名跨学科科学家聚集在一起，包括分子生物学家、免疫学家、计算 生物学家和老年病学家，他们在了解衰老和衰老相关疾病方面有着共同的兴趣 在系统层面上。许多与衰老相关的疾病，如癌症和心血管疾病， 受免疫系统功能障碍的影响。基因组图谱技术的最新进展(例如， 细胞转录学)提供了一个机会来发现人类细胞/组织中与衰老相关的变化，并将 这些对健康和寿命的改变。使用这些技术产生的数据的财富和复杂性是 不断增长，开发先进的计算方法来挖掘和整合这些数据的需求也越来越大。 尽管有这种需要，但目前还没有正式的场所让科学家，特别是那些在老龄化领域的科学家， 可以接受数据挖掘技术(即机器学习)的基础和应用方面的培训 算法)。此外，目前关于老龄化问题的会议并不旨在具体地将 计算生物学家、免疫学家以及基础和临床衰老研究人员。因此，其目标是 这次会议是：(1)承认并强调老龄领域高度跨学科的性质，并 促进和加速跨三个学科的思想协作和交叉授粉：老龄化， 免疫学和计算生物学；(2)为学员(学生和博士后研究员)提供 有机会与更资深的调查人员密切互动并获得反馈，以推进他们的项目 并建立联系以帮助他们建立职业生涯；以及(3)为研究人员提供机会 学习老龄化领域的机器学习技术的基础知识，他们将能够立即应用到 他们自己的研究回到了他们的家乡机构。我们会透过推行 遵循目标。在目标1中，我们将组织一次以老龄化为重点的跨学科会议和实践研讨会 以及与衰老相关的疾病。会议将包括一个为期两天的研讨会，主要由 科学家，随后是为期一天的机器学习基础实践研讨会。在目标2中，我们将推动 促进协作研究和职业发展的互动，包括通过海报会议。在AIM 3、我们将招募多样化的参会人员。我们建议的演讲者名单上有几位女科学家，我们将使用 我们的伙伴关系网络专门招募来自代表性不足的国家的种族和民族的与会者 组。会议的最终目标是通过加快合作来推动老龄化研究领域的发展 以及通过应用先进的数据挖掘方法来理解与衰老相关的基因组数据。