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

基本信息

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
项目类别：
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&apos 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

项目摘要

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.

项目概要/摘要本 R13 申请中要求的资金用于部分支持“衰老系统生物学：数据科学” 遇见老年科学”年会将于 2019 年至 2022 年每年 8 月/9 月在 The 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。共有多达 150 名跨学科科学家，包括分子生物学家、免疫学家、计算学家生物学家和老年病学家，他们对了解衰老和衰老相关疾病有着共同的兴趣许多与衰老相关的疾病，例如癌症和心血管疾病，都是在系统层面上发生的。受免疫系统功能障碍的影响。基因组分析技术的最新进展（例如，单一）。细胞转录组学）提供了揭示人类细胞/组织中与衰老相关的变化并将其联系起来的机会使用这些技术产生的数据的丰富性和复杂性是对健康和寿命的影响。开发先进计算方法来挖掘和整合这些数据的需求也在不断增加。尽管有这种需要，但目前还没有正式的场所可供科学家，特别是老龄化领域的科学家，可以接受数据挖掘技术（即机器学习）的基础知识和应用方面的培训此外，当前关于老龄化的会议并不是为了专门聚集在一起。因此，计算生物学家、免疫学家以及基础和临床衰老研究人员的目标。本次会议的目标是：（1）认识并强调老龄化领域的高度跨学科性质，并促进和加速三个学科之间的合作和思想交叉授粉：老龄化、免疫学和计算生物学；（2）为学员（学生和博士后）提供有机会与更高级的研究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发展职业生涯；(3) 为研究人员提供机会；老龄化领域学习机器学习技术的基础知识，他们将能够立即应用这些技术他们回到家乡机构后进行自己的研究，我们将通过开展这些目标来实现这些目标。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。会议将包括为期两天的研讨会，由主要演讲嘉宾进行演讲。在目标 2 中，我们将举办为期 1 天的机器学习基础实践研讨会。互动，以促进合作研究和职业发展，包括通过海报会议。 3、我们将招募多元化的与会者。我们提议的演讲者名单中有几位女性科学家，我们将使用这些女性科学家。我们的合作伙伴网络专门招募来自全国代表性不足的种族和民族的与会者会议的最终目标是通过加快合作推进老龄化研究领域。以及通过应用先进的数据挖掘方法了解与衰老相关的基因组数据。