HIGH PERFORMANCE BIOMEDICAL IMAGING COMPUTER RESOURCES

高性能生物医学成像计算机资源

• 批准号: 7498360
• 负责人: Fred William Prior
• 金额: $ 194.1万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-15 至 2010-04-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7498360
• 关键词: Architecture; Bioinformatics; Clinical Research; Communities; Computational Biology; Computer Systems; Computer software; Computers; Data; Data Set; Diffusion; Environment; Funding; Genomics; Health Insurance Portability and Accountability Act; High Performance Computing; Image; Imagery; Imaging technology; Institution; Link; Medical; Medical Imaging; Medical Research; Output; Performance; Privacy; Process; Productivity; Research; Research Project Grants; Resolution; Resources; Secure; Security; United States National Institutes of Health; Universities; Washington; bioimaging; cluster computing; distributed memory; fundamental research; imaging modality; medical schools; shared memory; supercomputer

DESCRIPTION (provided by applicant): Linking information at the genomic level with the vast quantities of phenotypic information derived from medical imaging is a fundamental research challenge that offers tremendous potential for near-term advances in medical treatment. To process the TeraBytes of data produced by new imaging technologies and genomic analyses, major medical research institutions such as Washington University turn to High Performance Computing (HPC) or supercomputers. There is a pressing need within the Washington University's Medical research community for a HPC located on the Medical School campus. Resources are currently restricted to two HPC systems which do not offer enhanced visualization capabilities or the combination of shared and distributed memory architectures. The establishment of the proposed HPC will achieve the following aims: 1) Provide a horizontal scaling capability which can support varying numbers of users each accessing the same application software in parallel. 2) Support NIH funded research projects such as Bayesian estimation of diffusion tensor parameters which require the functionality of a large shared memory system computing environment. 3) Allow remote use of image rendering and manipulation applications by sending the graphics display of the HPC's output to users located anywhere on the research network providing efficient visualization of large datasets at high resolution in a collaborative environment. 4) Accommodate privacy and security restrictions imposed by HIPAA while removing the complexities of transporting research data off campus to access HPC systems at other facilities or institutions. This HPC environment will provide a unique combination of high performance shared memory computing, large scale cluster computing and high performance distributed visualization all within a HIPPA secure environment and is essential for a large number of current NIH-funded projects. These capabilities will enable substantial increases in productivity for existing projects covering a broad range of imaging modalities while supporting Washington University's commitment to enhancing basic and clinical research in imaging, genomics, bioinformatics and computational biology.

描述（由申请人提供）：将基因组水平的信息与来自医学成像的大量表型信息联系起来是一项基本的研究挑战，为医学治疗的近期进展提供了巨大的潜力。为了处理新成像技术和基因组分析产生的万亿次数据，华盛顿大学等主要医学研究机构转向高性能计算（HPC）或超级计算机。华盛顿大学的医学研究迫切需要 位于医学院校园内的HPC社区。资源目前仅限于两个HPC系统，它们不提供增强的可视化功能或共享和分布式内存架构的组合。建议的高性能计算机的设立将达致以下目的：1）提供横向扩展能力，以支援不同数目的用户并行使用同一应用软件。2)支持NIH资助的研究项目，如 扩散张量参数的贝叶斯估计需要大型共享内存系统计算环境的功能。3)通过将HPC输出的图形显示发送给位于研究网络上任何位置的用户，允许远程使用图像渲染和操作应用程序，从而在协作环境中以高分辨率高效可视化大型数据集。4)适应HIPAA施加的隐私和安全限制，同时删除 将研究数据从校园传输到其他地方访问HPC系统的复杂性 设施或机构。这种HPC环境将提供高性能共享内存计算、大规模集群计算和高性能分布式计算的独特组合 可视化都在HIPPA安全的环境中进行，并且对于当前大量NIH资助的项目至关重要。这些能力将使现有项目的生产力大幅提高，涵盖广泛的成像模式，同时支持华盛顿大学的承诺，加强在成像，基因组学，生物信息学和计算生物学的基础和临床研究。

项目成果

Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network.

• DOI: 10.1021/acscentsci.5b00131
• 发表时间: 2015-07-22
• 期刊: ACS central science
• 影响因子: 18.2
• 作者: Hughes TB;Miller GP;Swamidass SJ
• 通讯作者: Swamidass SJ

Deep Learning Coordinate-Free Quantum Chemistry.

• DOI: 10.1021/acs.jpca.1c04462
• 发表时间: 2021-10-14
• 期刊: JOURNAL OF PHYSICAL CHEMISTRY A
• 影响因子: 2.9
• 作者: Matlock, Matthew K.;Hoffman, Max;Le Dang, Na;Folmsbee, Dakota L.;Langkamp, Luke A.;Hutchison, Geoffrey R.;Kumar, Neeraj;Sarullo, Kathryn;Swamidass, S. Joshua
• 通讯作者: Swamidass, S. Joshua

RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI.

• DOI: 10.1109/tmi.2012.2231873
• 发表时间: 2013-06
• 期刊: IEEE transactions on medical imaging
• 影响因子: 10.6
• 作者: Sotiropoulos SN;Jbabdi S;Andersson JL;Woolrich MW;Ugurbil K;Behrens TE
• 通讯作者: Behrens TE

Dual mechanisms suppress meloxicam bioactivation relative to sudoxicam.

• DOI: 10.1016/j.tox.2020.152478
• 发表时间: 2020-07
• 期刊: Toxicology
• 影响因子: 4.5
• 作者: Barnette DA;Schleiff MA;Osborn LR;Flynn N;Matlock M;Swamidass SJ;Miller GP
• 通讯作者: Miller GP

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites.

• DOI: 10.1021/acs.jcim.0c00360
• 发表时间: 2020-10-26
• 期刊: Journal of chemical information and modeling
• 影响因子: 5.6
• 作者: Hughes TB;Dang NL;Kumar A;Flynn NR;Swamidass SJ
• 通讯作者: Swamidass SJ