High Performance Biomedical Computing And Informatics

高性能生物医学计算和信息学

• 批准号: 7593224
• 负责人: calvin a johnson
• 金额: $ 236.06万
• 依托单位: CENTER FOR INFORMATION TECHNOLOGY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7593224
• 关键词: Address; Adopted; Algorithms; Biological; Biomedical Computing; Biomedical Research; Biometry; Cells; Characteristics; Chromosome Mapping; Chromosomes; Clinic; Clinical; Communication; Complex; Computer Analysis; Computer Workstations; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Databases; Environment; Equilibrium; Expressed Sequence Tags; Gene Expression; Goals; Hereditary Disease; High Performance Computing; Imagery; Informatics; Information Management; Information Systems; Information Technology; Internet; Knowledge; Laboratories; Life; Literature; Management Information Systems; Medical Imaging; Modeling; Molecular Biology; Monitor; Numbers; Pattern; Performance; Process; Purpose; Range; Research; Research Infrastructure; Research Personnel; Resources; Science; Simulate; Software Engineering; Software Tools; Source; Speed; System; Techniques; Technology; Time; United States National Institutes of Health; biomedical scientist; computing resources; data management; data mining; design; genetic linkage analysis; genome sequencing; human population genetics; image processing; knowledge base; novel strategies; parallel architecture; parallel computing; programs; repository; tool; vector; web-accessible

Using high-performance, parallel computing and knowledge-based data management systems, biomedical scientists can greatly reduce the time it takes to complete computationally intensive tasks, adopt new approaches for processing experimental data, and mine large, complex datasets to find important data patterns. These benefits may allow for the inclusion of more data into a calculation, the determination of a more accurate result, a reduction in the time needed to complete a long calculation, or the implementation of a new algorithm or a more realistic model. High-performance parallel computing allows biomedical scientists to analyze and study large datasets that cannot be processed within a practical amount of computer analysis time on conventional sequential or vector processing computing machines. With high bandwidth network connections and interactive user interfaces, parallel computing is readily accessible to a biomedical researcher in the laboratory or clinic at the investigator's computer workstation. Knowledge-based databases and data mining tools are powerful resources in modern biomedical research. They provide researchers ways to extract and use information from vast amounts of knowledge and data collected from wide-ranging sources such as public and commercial databases (e.g., genome sequences, chromosome, SNP, genetic diseases, EST cluster, gene mapping, gene expression, molecular biology, and literature databases). In addressing these complex data analysis and management challenges, the High Performance Computing and Informatics Office (HPCIO) of the Division of Computational Bioscience (DCB) in the Center for Information Technology (CIT) is developing high performance computing hardware and software infrastructure for a wide range of biomedical applications where computational speed, advanced data analysis, smart data mining and large-scale data management are important. These include gene expression data analysis, image processing of live-cell arrays, medical image and clinical information management, biostatistics, population genetics, and human genetic linkage analysis. The ultimate goal is to provide high performance parallel computing, scientific databases, and data analysis and visualization tools to facilitate the science that is done at the NIH. While developing the computationally demanding applications and scientific database systems, HPCIO is investigating the following challenging issues: (1) partitioning a problem into many parts that can be executed independently on different processors; (2) designing the parts so that the computing load can be distributed evenly or dynamically balanced over the available processors; (3) designing algorithms so that the number of processors is a parameter and the algorithms can be configured dynamically for the available machine; (4) developing tools and environments for producing portable parallel programs; (5) incorporating interactive data analysis and visualization tools into the user environment; (6) monitoring system performance; (7) proving that a parallel algorithm on a given machine meets its specifications; (8) evaluating modern parallel computer architectures for their performance characteristics on biomedical applications; and (9) developing and providing high performance database systems and data mining tools for the archival and analysis of scientific data and medical images via web-accessible interfaces.

使用高性能，并行计算和基于知识的数据管理系统，生物医学科学家可以大大减少完成计算密集型任务所需的时间，采用新方法来处理实验数据，并挖掘大型，复杂的数据集以找到重要的数据模式。这些好处可能允许将更多的数据包括在计算中，确定更准确的结果，减少完成长计算所需的时间，或实现新算法或更现实的模型。高性能并行计算允许生物医学科学家分析和研究大型数据集，这些数据集无法在常规顺序或矢量处理计算机上进行实用数量的计算机分析时间内处理。借助高带宽网络连接和交互式用户界面，在研究人员的计算机工作站的实验室或诊所的生物医学研究人员可以轻松访问并行计算。基于知识的数据库和数据挖掘工具是现代生物医学研究中的强大资源。他们提供研究人员从广泛的来源收集的大量知识和数据中提取和使用信息的方法，例如公共和商业数据库（例如，基因组序列，染色体，SNP，遗传疾病，EST群集，基因映射，基因表达，分子生物学和文献数据库）。在应对这些复杂的数据分析和管理挑战时，在信息技术中心（CIT）中，计算生物科学（DCB）的高性能计算和信息办公室（HPCIO）正在开发高性能计算硬件和软件基础架构，用于广泛的生物医学应用程序，其中计算速度，高级数据分析，智能数据挖掘和大型数据管理很重要。其中包括基因表达数据分析，实时细胞阵列的图像处理，医学图像和临床信息管理，生物统计学，人群遗传学和人类遗传链接分析。最终目标是提供高性能并行计算，科学数据库以及数据分析和可视化工具，以促进NIH进行的科学。 HPCIO在开发计算苛刻的应用程序和科学数据库系统时正在研究以下具有挑战性的问题：（1）将问题划分为可以在不同的处理器上独立执行的许多部分； （2）设计零件，以便可以在可用处理器上均匀或动态平衡计算负载； （3）设计算法，以便处理器的数量是参数，并且可以为可用的机器动态配置算法； （4）开发用于生产便携式并行程序的工具和环境； （5）将交互式数据分析和可视化工具纳入用户环境； （6）监视系统性能； （7）证明给定机器上的平行算法符合其规格； （8）评估现代平行计算机架构的生物医学应用的性能特征； （9）开发和提供高性能数据库系统和数据挖掘工具，以通过Web访问的接口对科学数据和医学图像进行档案和分析。