Cross Training Core

交叉训练核心

基本信息

批准号：
10515455
负责人：
Luigi Marchionni
金额：
$ 20.93万
依托单位：
UNIVERSITY OF MARYLAND BALTIMORE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-04 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10515455
关键词：
Address Algorithmic Software Basic Science Big Data Bioinformatics Biology Biometry Clinical Sciences Collaborations Communities Complex Computational Biology Computing Methodologies Data Data Science Data Storage and Retrieval Development Elements Ensure Evolution Faculty Fostering Generations Genomics Image Image Analysis Institution Investigation Laboratories Laboratory Scientists Learning Light Liquid substance Malignant Neoplasms Malignant neoplasm of prostate Medicine Methodology Methods Mission Modernization Molecular Analysis Pathology Persons Population Positioning Attribute Process Production Radiation Oncology Recording of previous events Reproducibility Research Research Design Research Personnel Research Project Grants Resources Role Scientist Standardization Systems Biology Technology Training Training Activity Work algorithmic methodologies analytical method cloud based data integration data sharing educational atmosphere experience file format genome sciences genomic data hands-on learning in situ imaging innovation interest medical schools meetings member metabolomics multimodal data multimodality novel programs radiomics tool transcriptomics

项目摘要

The Cross-Training Core (CTC) for the U54 ROBIN OligoMET Center will consist of experienced faculty members of the Division of Computational and Systems Biology (CSP) in the Department of Pathology and Laboratory Medicine of Weill Cornell Medical College (WCM). The team at WCM will work in close collaboration with teams at the other Institutions, and a history of collaboration already exists between the CTC team members and the investigators leading the Research Projects and the other Research Cores, which will further ensure the successful progression of the Center. The CTC activities will be centered around the following purposes: Aim 1) To provide a unified analytical framework across the U54 ROBIN OligoMET Center to foster cross-project data integration and comparison. Imaging, omics, and radiomics data are found in a variety of forms (e.g., different platforms, file formats, etc.), and training people on how to manage each of these instances is challenging and inefficient. We will have unified analytical framework within the U54 ROBIN OligoMET Center where data is aggregated and standardized, greatly decreases the training complexity and increases reproducibility which results in a more fluid training process. Aim 2) To provide educational support and training across the U54 ROBIN OligoMET Center. Advances in genomics, transcriptomics, metabolomics, and radiomics technologies have led to exponential rises in both production and availability of multimodal data. In light of these rapid evolutions, disseminating the latest bioinformatics methods within the U54 Center – and the broader biomedical community – is a challenge of paramount importance. The CTC will address such challenge by creating an open educational platform that will provide a rich interactive learning environment, leveraging a cloud-based framework to collaboratively create and share tutorials and learning experiences. To this end, the CTC will build upon a 10- year experience in the computational genomics and data science domains and training biologists and clinicians in computational methods. Aim 3) To develop novel analytical approaches for the comprehensive characterization of oligometastatic prostate cancer (PCa) via integrated analyses of multimodal big data. Omics and radiomics technologies, multiparametric in situ imaging, and spatially-resolved molecular and image analyses are rapidly evolving fields. Therefore, continually evolving technologies, software, algorithms, and analytical methods are efforts of essence. PCa investigations across the U54 ROBIN OligoMET Center encompass a multitude of these domains, hence it is of paramount importance that a versatile and innovative portfolio of approaches is developed to fully support the ongoing and future research. The U54 ROBIN OligoMET Center will therefore provide an ideal platform for such cross-disciplinary training, and the CTC will support such crucial endeavor through developing and disseminating ad-hoc training modules across the whole U54 ROBIN OligoMET Center and the other ROBIN Centers.

U54 ROBIN OligoMET 中心的交叉培训核心 (CTC) 将由经验丰富的教师组成病理学系计算和系统生物学部 (CSP) 成员威尔康奈尔医学院 (WCM) 的检验医学团队将密切合作。与其他机构的团队合作，并且 CTC 团队成员之间已经存在合作历史以及领导研究项目和其他研究核心的研究人员，这将进一步确保该中心的成功发展将围绕以下目标展开：目标 1) 为整个 U54 ROBIN OligoMET 中心提供统一的分析框架，以促进跨项目数据成像、组学和放射组学数据有多种形式（例如，不同的形式）。平台、文件格式等），并且培训人员如何管理这些实例是一项具有挑战性的工作我们将在 U54 ROBIN OligoMET 中心内建立统一的分析框架，其中的数据是低效的。聚合和标准化，大大降低了训练复杂性并提高了可重复性目标 2) 为整个 U54 ROBIN 提供教育支持和培训。 OligoMET 中心在基因组学、转录组学、代谢组学和放射组学技术方面取得了领先地位。鉴于这些快速发展，多模式数据的产生和可用性呈指数级增长。在 U54 中心以及更广泛的生物医学界传播最新的生物信息学方法 – 是一项至关重要的挑战，CTC 将通过创建开放式教育来应对这一挑战。该平台将提供丰富的交互式学习环境，利用基于云的框架为此，CTC 将建立在 10- 在计算基因组学和数据科学领域以及培训生物学家和战士方面拥有一年的经验目标 3）开发新的综合分析方法。通过多模式大数据的综合分析来表征寡转移性前列腺癌（PCa）。和放射组学技术、多参数原位成像以及空间分辨分子和图像因此，分析是快速发展的领域，技术、软件、算法和技术也在不断发展。分析方法是 U54 ROBIN OligoMET 中心的 PCa 研究的核心成果。涵盖多个领域，因此至关重要的是，一个多功能且创新的 U54 ROBIN OligoMET 的开发方法组合是为了全力支持正在进行的和未来的研究。因此，中心将为此类跨学科培训提供理想的平台，CTC将支持此类培训通过在整个 U54 ROBIN 中开发和传播临时培训模块来做出至关重要的努力 OligoMET 中心和其他 ROBIN 中心。