Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
California Institute of Technology, Pasadena, CA, USA.
Cancer Biomark. 2022;33(4):479-488. doi: 10.3233/CBM-210350.
NASA's Jet Propulsion Laboratory (JPL) is advancing research capabilities for data science with two of the National Cancer Institute's major research programs, the Early Detection Research Network (EDRN) and the Molecular and Cellular Characterization of Screen-Detected Lesions (MCL), by enabling data-driven discovery for cancer biomarker research. The research team pioneered a national data science ecosystem for cancer biomarker research to capture, process, manage, share, and analyze data across multiple research centers. By collaborating on software and data-driven methods developed for space and earth science research, the biomarker research community is heavily leveraging similar capabilities to support the data and computational demands to analyze research data. This includes linking diverse data from clinical phenotypes to imaging to genomics. The data science infrastructure captures and links data from over 1600 annotations of cancer biomarkers to terabytes of analysis results on the cloud in a biomarker data commons known as "LabCAS". As the data increases in size, it is critical that automated approaches be developed to "plug" laboratories and instruments into a data science infrastructure to systematically capture and analyze data directly. This includes the application of artificial intelligence and machine learning to automate annotation and scale science analysis.
美国国家航空航天局（NASA）的喷气推进实验室（JPL）正在通过两个国家癌症研究所的主要研究计划，即早期检测研究网络（EDRN）和筛查检测病变的分子和细胞特征（MCL），推进数据科学的研究能力，为癌症生物标志物研究实现数据驱动的发现。该研究团队开创了一个国家癌症生物标志物研究的数据科学生态系统，以捕获、处理、管理、共享和分析来自多个研究中心的数据。通过合作开发用于太空和地球科学研究的软件和数据驱动方法，生物标志物研究社区正在大力利用类似的功能来支持分析研究数据的需求。这包括将来自临床表型的不同数据与成像和基因组学联系起来。数据科学基础设施在一个名为“LabCAS”的生物标志物数据公共库中捕获并链接了超过 1600 个癌症生物标志物注释的数据，并将分析结果链接到云平台上的兆字节。随着数据规模的增加，开发自动化方法将实验室和仪器“插入”数据科学基础设施以直接系统地捕获和分析数据变得至关重要。这包括应用人工智能和机器学习来自动注释和扩展科学分析。
