课题基金 / 基金详情

项目摘要

项目成果

Shuangge Ma的其他基金

相似基金

相关文献

中文摘要
翻译
核心B:项目总结 生物统计和生物信息学核心的主要目标是与所有YSILC(耶鲁大学孢子in 肺癌)调查人员,以满足个别项目产生的分析需求。在本奖项中 在此期间,核心一直是青海省国际法委员会的一个关键和有效的组成部分。在接下来的颁奖期间,我们将 进一步加大力度。我们将保持我们的开放政策,并与所有YSILC调查人员就 定期的。核心将继续在实现项目目标方面发挥关键作用,确保所有 研究经过严格的设计、执行、分析和报告。我们还将在数据方面发挥重要作用 管理并确保所有数据都得到适当的管理和保护。美国国立卫生研究院关于数据发布的所有指南 分享将得到适当的遵循。Core还将对YSILC和更广泛的肺癌做出贡献 通过开发更有效的分析以及参与培训和教育,促进社区发展。 具体目标如下。目标1:为所有人提供强有力的生物统计学和生物信息学支持 YSILC项目和调查人员。核心将与所有人保持定期和动态的互动 调查人员。我们将向项目、其他核心和通过以下方式资助的项目的所有调查人员开放 发展研究计划(DRP)和职业提升计划(CEP)。核心一直是 并将继续积极参与研究设计的整个范围。在执行过程中,我们将确保 计划得到了严格的遵守。我们将密切关注研究进展,定期进行监测和分析, 如果需要,还可以重新审查/修改研究设计。数据收集完成后,我们将进行全面的 使用现有的和新的方法进行分析,并协助准备手稿、摘要、海报和 批准申请。目标2:为所有项目提供有效的数据管理。我们的核心,以及 管理和Biospecimen核心,将使用 中央数据管理系统,这将减轻个别调查人员的数据管理负担 和项目,并确保收集的数据的一致性。我们将确保下游分析是 在这一过程中充分考虑到了这一点,并适当遵守了NIH的所有数据共享规定,其中 包括将经过适当管理的数据存放到公共存储库。目标3:发展创新的生物统计学和 生物信息学方法。核心已经并将继续开发和实施最先进的新技术 针对肺癌数据量身定制的分析方法。这项工作将有助于更有效地利用青海土地利用中心 数据,促进肺癌分析研究,造福广大研究界。 核心将由赵宏宇博士(生物信息学)和马双阁博士(生物统计学)共同领导。一支出色的球队 已经组建,拥有丰富的经验和所有必要的专业知识。
英文摘要
CORE B: PROJECT SUMMARY The main objective of the Biostatistics and Bioinformatics Core is to collaborate with all YSILC (Yale SPORE in Lung Cancer) investigators to address analytical needs arising from individual projects. In the present award period, the Core has been a critical and effective component of the YSILC. In the next award period, we will further strengthen our effort. We will keep our open door policy and interact with all YSILC investigators on a regular basis. The Core will keep playing a critical role in accomplishing the projects’ goals by ensuring that all studies are rigorously designed, executed, analyzed, and reported. We will also take an important role in data management and ensure that all data are properly managed and protected. All NIH guidelines on data publication and sharing will be properly followed. The Core will also contribute to the YSILC and broader lung cancer community by developing more effective analytics and by being involved in training and education. The specific aims are as follows. Aim 1: Provide strong biostatistical and bioinformatics support to all YSILC projects and investigators. The Core will maintain regular and dynamic interactions with all investigators. We will be available to all investigators of the projects, other Cores, and projects funded through the Developmental Research Program (DRP) and Career Enhancement Program (CEP). The Core has been and will remain actively involved in the whole spectrum of study design. In execution, we will ensure that the plans are rigorously followed. We will closely monitor study progress, conduct regular monitoring and analysis, and revisit/revise study designs if needed. After data collection is completed, we will conduct comprehensive analysis using existing as well as new methods and assist in preparing manuscripts, abstracts, posters, and grant applications. Aim 2: Provide effective data management for all projects. Our Core, along with the Administrative and Biospecimen Cores, will offer cost-effective and efficient data management services using a centralized data management system, which will reduce data management burden for individual investigators and projects and also guarantee the uniformity of collected data. We will ensure that downstream analyses are fully taken into consideration in the process and that all NIH data-sharing regulations are properly followed, which includes depositing properly curated data to public repositories. Aim 3: Develop innovative biostatistical and bioinformatics methods. The Core has been and will keep developing and implementing state-of-the-art new analysis methods tailored to lung cancer data. This effort will facilitate more effective utilization of the YSILC data, foster lung cancer analytic research, and benefit the broad research community. The Core will be co-led by Drs. Hongyu Zhao (bioinformatics) and Shuangge Ma (biostatistics). A stellar team has been assembled, with extensive experiences and all the necessary expertise.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Cancer Emulation Analysis with Deep Neural Network
  • 批准号:
    10725293
  • 项目类别:
  • 资助金额:
    $16.75万
  • 财政年份:
    2023
  • 负责人:
    Shuangge Ma
  • 依托单位:
Deep Learning-based Emulation Analysis: Methodological Developments and Case Studies
  • 批准号:
    10515491
  • 项目类别:
  • 资助金额:
    $12.56万
  • 财政年份:
    2022
  • 负责人:
    Shuangge Ma
  • 依托单位:
Deep Learning-based Emulation Analysis: Methodological Developments and Case Studies
  • 批准号:
    10676303
  • 项目类别:
  • 资助金额:
    $12.56万
  • 财政年份:
    2022
  • 负责人:
    Shuangge Ma
  • 依托单位:
Integrated Cancer Modeling: A New Dimension
  • 批准号:
    9812144
  • 项目类别:
  • 资助金额:
    $8.38万
  • 财政年份:
    2019
  • 负责人:
    Shuangge Ma
  • 依托单位:
海外基金