Cross Training Core

U 54 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（CSP）部门的成员， 威尔康奈尔医学院（Weill Cornell Medical College）WCM的团队将密切合作， 与其他机构的团队合作，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 中心的成功发展。反恐委员会的活动将围绕以下目标展开： 在U 54 ROBIN OligoMET中心提供统一的分析框架，以促进跨项目数据 整合与比较。成像、组学和放射组学数据以各种形式存在（例如，不同 平台、文件格式等），培训人们如何管理这些实例是一项挑战， 效率低下。我们将在U 54 ROBIN OligoMET中心内建立统一的分析框架， 聚合和标准化，大大降低了训练的复杂性，并增加了可重复性， 从而使训练过程更加流畅。目标2）在U 54 ROBIN提供教育支持和培训 OligoMET中心。基因组学、转录组学、代谢组学和放射组学技术的进步 多模式数据的生产和可用性呈指数级增长。鉴于这些快速发展， 在U 54中心和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。反恐委员会将通过建立一个开放的教育体系来应对这一挑战。 该平台将提供丰富的交互式学习环境，利用基于云的框架， 协作创建和共享教程和学习经验。为此目的，反恐委员会将在10- 在计算基因组学和数据科学领域以及培训生物学家和临床医生方面有一年的经验 在计算方法中。目的3）开发新的分析方法， 通过多模态大数据的综合分析来表征寡转移性前列腺癌（PCa）。组学 和放射组学技术，多参数原位成像，空间分辨分子和图像 分析是快速发展的领域。因此，不断发展的技术、软件、算法和 分析方法是本质上的努力。U 54 ROBIN OligoMET中心的PCa研究 涵盖了众多的这些领域，因此，至关重要的是，一个多才多艺和创新的 开发了一系列方法，以充分支持正在进行的和未来的研究。U54 ROBIN OligoMET 因此，中心将为这种跨学科培训提供一个理想的平台，反恐中心将支持这种跨学科培训。 通过在整个U 54 ROBIN中开发和传播特设培训模块， OligoMET中心和其他罗宾中心。

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.