Cross Training Core

U54 Robin OlicoMet中心的交叉培训核心(CTC)将由经验丰富的教师组成 病理与系统生物学系计算与系统生物学系成员 威尔康奈尔医学院(WCM)的检验医学。WCM的团队将密切合作 与其他机构的团队合作，反恐委员会团队成员之间已经存在合作的历史 以及领导研究项目的调查人员和其他研究核心，这将进一步确保 该中心的成功发展。反恐委员会的活动将围绕以下目的展开：目标1) 在U54 Robin OlicoMET中心提供统一的分析框架，以促进跨项目数据 整合和比较。成像、组学和放射组学数据以各种形式(例如，不同的 平台、文件格式等)，并培训人员如何管理这些实例，这是具有挑战性的 效率低下。我们将在U54 Robin OdonoMet中心内建立统一的分析框架，数据位于该中心 聚合和标准化，大大降低了训练的复杂性，增加了可重复性 结果是训练过程更加流畅。目标2)为U54知更鸟提供教育支持和培训 寡头MET中心。基因组学、转录组学、代谢组学和放射组学技术的进步引领了 到多模式数据的生产和可获得性的指数级增长。鉴于这些快速的进化， 在U54中心和更广泛的生物医学社区内传播最新的生物信息学方法 -是一项至关重要的挑战。反恐委员会将通过创建一个开放的教育机构来应对这一挑战 将提供丰富的交互式学习环境的平台，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 在计算方法上。目的3)开发新的分析方法，以全面 通过多模式大数据的综合分析来描述前列腺癌(PCA)的特征。组学 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 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。在U54 Robin OdonoMet中心进行PCA调查 涵盖了这些领域的众多领域，因此，最重要的是多才多艺和创新 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罗宾寡聚MET 因此，中心将为这种跨学科培训提供一个理想的平台，反恐委员会将支持这样的培训 通过在整个U54 Robin中开发和传播特别培训模块的关键努力 奥莱戈梅特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.