CISE Research Instrumentation: A Distributed Biomedical Computing Laboratory

CISE 研究仪器：分布式生物医学计算实验室

• 批准号: 9617349
• 负责人: Lubomir Bic
• 金额: $ 7万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-03-01 至 1998-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9617349&HistoricalAwards=false
• 关键词: CISE; Research; Instrumentation; Distributed; Biomedical

9617349 Bic, Lubomir, F. Michael Dillencourt University of California, Irvine CISE Research Instrumentation: A Distributed Biomedical Computing Laboratory Many important problem areas of biomedical computing have the ability to generate large volumes of relatively coarse-grain computations and thus can benefit greatly from a LAN-based network of workstations. This project will provide such an infrastructure, based on a cluster of Sun workstations, which will be shared by the following four projects: -Knowledge Discovery in Clinical Databases-Brain Network Models-Toxicology simulations-Distributed Protein Structure Prediction. The goal of this project is to discover new knowledge in clinical databases that will be useful for diagnosis and treatment. It uses novel machine learning methods that are particularly suited to medical databases. The second project attempts to understand the functions of specific brain regions. It will develop parallel versions of neural-net-based algorithms to solve difficult signal-processing tasks. The third project develops a distributed simulation environment that supports the construction and interactive use of simulation models for predicting concentrations of chemicals (drugs or toxins) in the human body to assess health risks. The last project will parallelize a novel protein structure prediction algorithm developed by our research group, and, subsequently, will validate a novel Bayes-optimal Prediction theory.

9617349比克，卢博米尔，F.迈克尔·迪伦古，加州大学欧文分校的研究仪器：分布式生物医学计算实验室生物医学计算的许多重要问题领域都有能力生成大量相对粗粒度的计算，因此可以从基于局域网的工作站网络中受益匪浅。该项目将提供这样一个基于Sun工作站集群的基础设施，将由以下四个项目共享：-临床数据库中的知识发现-大脑网络模型-毒理学模拟-分布式蛋白质结构预测。该项目的目标是在临床数据库中发现对诊断和治疗有用的新知识。它使用了特别适合医学数据库的新型机器学习方法。第二个项目试图了解特定大脑区域的功能。它将开发基于神经网络的算法的并行版本，以解决困难的信号处理任务。第三个项目开发了一个分布式模拟环境，支持构建和交互使用模拟模型，以预测化学物质(药物或毒素)在人体内的浓度，以评估健康风险。最后一个项目将并行化我们课题组开发的一种新的蛋白质结构预测算法，并随后验证一种新的贝叶斯最优预测理论。