Adapting the Berkeley Big Data Analytics Stack to Genomics and Health

将伯克利大数据分析堆栈应用于基因组学和健康

• 批准号: 9466681
• 负责人: Alexander Wait Zaranek
• 金额: $ 108.67万
• 依托单位: CUROVERSE INNOVATIONS, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9466681
• 关键词: Adoption; Algorithms; Apache; Big Data; Big Data to Knowledge; Businesses; Capital; Clinical; Clinical Decision Support Systems; Cloud Computing; Collaborations; Collection; Communities; Computer software; Contractor; Data; Data Analytics; Data Set; Distributed Systems; Ecosystem; Ensure; Feedback; Funding; Genome; Genomics; Health; Individual; Industrialization; Industry; Ingestion; Institutes; International; Leadership; Letters; Machine Learning; Maintenance; Measures; Medicine; Molecular; Performance; Phase; Phenotype; Policies; Production; Provider; Publications; Resources; Running; Secure; Services; Small Business Innovation Research Grant; Software Tools; Source; System; Technology; Time; Training; Variant; Work; base; cloud platform; cluster computing; commercialization; design; distributed data; genomic data; health care delivery; individual patient; innovation; open source; operation; petabyte; precision medicine; symposium; web services; whole genome

Project Summary We propose building a computational platform based on the high performance Berkeley Big Data Analytics Stack (BDAS) to support a new ecosystem of Clinical Decision Support (CDS) applications. This platform will make it faster, easier, and less expensive to develop molecular Clinical Decision Support Systems. These systems require real-time queries of globally distributed data, efficient machine learning on large genomic datasets, and must be secure, fault-tolerant and scalable. BDAS and associated technologies are designed to help us meet these challenges and are therefore ideal building blocks to help us create our computational platform. To encourage the adoption of standards for the querying and sharing of large genomic datasets, we will adapt the BDAS stack to support the standards of the Global Alliance for Genomics and Health (GA4GH).

项目摘要 我们建议建立一个基于高性能伯克利大的计算平台 数据分析堆栈（BDA）支持临床决策支持的新生态系统（CDS） 申请。该平台将使开发分子更快，更容易且价格较低 临床决策支持系统。这些系统需要全球实时查询 分布式数据，大型基因组数据集上有效的机器学习，必须安全， 容忍和可扩展性。 BDA和相关技术旨在帮助我们见面 这些挑战，因此是帮助我们创建计算的理想基础 平台。鼓励采用标准来查询和共享大型 基因组数据集，我们将调整BDAS堆栈以支持全球联盟的标准 用于基因组学和健康（GA4GH）。