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中文摘要
翻译
核心B:项目总结 生物统计学和生物信息学核心的主要目标是与所有YSILC(Yale SPORE)合作, 肺癌)研究者,以解决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YSILC的关键和有效组成部分。在下一个奖项期间,我们将 进一步加强我们的努力。我们将保持我们的门户开放政策,并与所有YSILC调查人员进行互动, 定期.核心将继续在实现项目目标方面发挥关键作用, 研究是严格设计、执行、分析和报告的。我们还将在数据方面发挥重要作用 确保所有数据得到妥善管理和保护。关于数据发布的所有NIH指南 共享将得到妥善遵循。核心也将有助于YSILC和更广泛的肺癌 通过开发更有效的分析以及参与培训和教育,社区。 具体目标如下。目标1:为所有人提供强有力的生物统计和生物信息学支持 YSILC项目和调查员。核心将与所有人保持定期和动态的互动, investigators.我们将提供给项目的所有调查人员,其他核心,以及通过 发展研究计划(DRP)和职业提升计划(CEP)。核心已经 并将继续积极参与整个研究设计范围。在执行过程中,我们将确保 计划得到严格执行。我们会密切监察研究进展,定期进行监察及分析, 并在必要时重新访问/修订研究设计。数据收集完成后,我们将进行全面的 分析使用现有的以及新的方法,并协助准备手稿,摘要,海报, 补助金申请。目标2:为所有项目提供有效的数据管理。我们的核心,沿着 行政和生物标本核心,将提供具有成本效益和高效的数据管理服务,使用 中央数据管理系统,这将减轻调查员个人的数据管理负担 并保证了采集数据的一致性。我们将确保下游分析 在此过程中充分考虑,并确保所有NIH数据共享法规得到适当遵守, 包括将适当管理的数据存放到公共存储库。目标3:开发创新的生物统计和 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 针对肺癌数据的分析方法。这项工作将有助于更有效地利用YSILC 数据,促进肺癌分析研究,并使广泛的研究社区受益。 核心将由Hongyu Zhao博士(生物信息学)和Shuangge Ma博士(生物统计学)共同领导。一个明星团队 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.
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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