CC*IIE Networking Infrastructure: Data Driven Expansion at The University of South Alabama

CC*IIE 网络基础设施：南阿拉巴马大学的数据驱动扩展

基本信息

批准号：
1440784
负责人：
Alec Yasinsac
金额：
$ 49.73万
依托单位：
University of South Alabama
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-01 至 2016-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1440784&HistoricalAwards=false
关键词：
CC IIE Networking Infrastructure Data

项目摘要

The project's purpose is to expand the University of South Alabama Cyberinfrastructure to improve collaborative research capability between scientists in the Mitchell Cancer Institute and the School of Computing. Infrastructure improvements are necessary to handle the high volume data transfer required for large databases and imagery data that form the core of this research.Research enabled by these campus network improvements includes investigations into the degree to which descriptive data driven analysis methods can discover relationships among groups of cancer mutations across user-defined tumor groupings. Because the traditional hallmarks of cancer commonly arise in diverse forms of cancer, mutational interplay between seemingly unrelated genes must exist and must play a role in fine tuning tumor behavior to its environment. By employing a data driven approach, where each tumor genome is viewed as an independent evolution experiment, it is possible to evaluate the unique mutational signatures composed predominately of passenger mutations, in addition to the traditional focus on driver mutations. This project increases underlying data networking capacity by a factor of 10, and results in benefits to research and education across the University of South Alabama by transforming the campus core network to a high performance 10Gbps network.

该项目的目的是扩大南阿拉巴马大学的网络基础设施，以提高米切尔癌症研究所和计算学院科学家之间的协作研究能力。对于处理构成本研究核心的大型数据库和图像数据所需的大量数据传输所需的基础设施改进是必要的。这些校园网络改进对研究进行了研究，包括研究描述性数据驱动分析方法可以发现跨用户定义的肿瘤分组的癌症突变之间的关系的程度。由于癌症的传统标志通常是在各种形式的癌症形式出现的，因此看似无关的基因之间的突变相互作用必须存在，并且必须在将肿瘤行为带入环境中发挥作用。通过采用数据驱动的方法，每个肿瘤基因组都被视为一个独立的进化实验，还可以评估主要由乘客突变组成的独特突变特征，除了传统的对驾驶员突变的重点。该项目将潜在的数据网络能力提高了10倍，并通过将校园核心网络转换为高性能10GBPS网络，从而为整个阿拉巴马大学的研究和教育带来好处。