HPCS 2012教程:教程一:生物医学信息学中的高性能计算

H. Ali, L. Ricci, I. Epicoco, M. Ujaldón, Jonathan Passerat-Palmbach, D. Hill, M. Villén-Altamirano
{"title":"HPCS 2012教程:教程一:生物医学信息学中的高性能计算","authors":"H. Ali, L. Ricci, I. Epicoco, M. Ujaldón, Jonathan Passerat-Palmbach, D. Hill, M. Villén-Altamirano","doi":"10.1109/HPCSim.2012.6266878","DOIUrl":null,"url":null,"abstract":"The last decade has witnessed significant developments in various aspects of Biomedical Informatics, including Bioinformatics, Medical Informatics, Public Health Informatics, and Biomedical Imaging. The explosion of medical and biological data requires an associated increase in the scale and sophistication of the automated systems and intelligent tools to enable the researchers to take full advantage of the available databases. The availability of vast amount of biological data continues to represent unlimited opportunities as well as great challenges in biomedical research. Developing innovative data mining techniques and clever parallel computational methods to implement them will surely play an important role in efficiently extracting useful knowledge from the raw data currently available. The proper integration of carefully selected/developed algorithms along with efficient utilization of High Performance Computing (HPC) systems form the key ingredients in the process of reaching new discoveries from biological data. This tutorial focuses on addressing several key issues related to the effective utilization of HPC in biomedical informatics research, in particular, how to efficiently utilize high performance systems in the analysis of massive biological data. A major issue is that regard is how to design energy-aware models for executing computationally-intensive biomedical applications on HPC systems. Another key issue is how to develop innovative network filters for massive biological networks that would utilize parallel algorithms to construct networks samplers that would preserve original network structures while uncovering new ones.","PeriodicalId":428764,"journal":{"name":"2012 International Conference on High Performance Computing & Simulation (HPCS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"HPCS 2012 tutorials: Tutorial I: High performance computing in biomedical informatics\",\"authors\":\"H. Ali, L. Ricci, I. Epicoco, M. Ujaldón, Jonathan Passerat-Palmbach, D. Hill, M. Villén-Altamirano\",\"doi\":\"10.1109/HPCSim.2012.6266878\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The last decade has witnessed significant developments in various aspects of Biomedical Informatics, including Bioinformatics, Medical Informatics, Public Health Informatics, and Biomedical Imaging. The explosion of medical and biological data requires an associated increase in the scale and sophistication of the automated systems and intelligent tools to enable the researchers to take full advantage of the available databases. The availability of vast amount of biological data continues to represent unlimited opportunities as well as great challenges in biomedical research. Developing innovative data mining techniques and clever parallel computational methods to implement them will surely play an important role in efficiently extracting useful knowledge from the raw data currently available. The proper integration of carefully selected/developed algorithms along with efficient utilization of High Performance Computing (HPC) systems form the key ingredients in the process of reaching new discoveries from biological data. This tutorial focuses on addressing several key issues related to the effective utilization of HPC in biomedical informatics research, in particular, how to efficiently utilize high performance systems in the analysis of massive biological data. A major issue is that regard is how to design energy-aware models for executing computationally-intensive biomedical applications on HPC systems. Another key issue is how to develop innovative network filters for massive biological networks that would utilize parallel algorithms to construct networks samplers that would preserve original network structures while uncovering new ones.\",\"PeriodicalId\":428764,\"journal\":{\"name\":\"2012 International Conference on High Performance Computing & Simulation (HPCS)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on High Performance Computing & Simulation (HPCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HPCSim.2012.6266878\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on High Performance Computing & Simulation (HPCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPCSim.2012.6266878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

过去十年见证了生物医学信息学各个方面的重大发展,包括生物信息学、医学信息学、公共卫生信息学和生物医学成像。医学和生物数据的爆炸式增长需要相应的自动化系统和智能工具的规模和复杂程度的增加,以使研究人员能够充分利用可用的数据库。大量生物数据的可用性继续代表着生物医学研究的无限机会和巨大挑战。开发创新的数据挖掘技术和巧妙的并行计算方法来实现这些技术,对于有效地从现有的原始数据中提取有用的知识将发挥重要作用。精心选择/开发的算法的适当整合以及高性能计算(HPC)系统的有效利用构成了从生物数据中获得新发现的关键因素。本教程的重点是解决与高性能计算在生物医学信息学研究中的有效利用有关的几个关键问题,特别是如何有效地利用高性能系统来分析大量生物数据。这方面的一个主要问题是如何设计能源感知模型,以便在高性能计算系统上执行计算密集型生物医学应用。另一个关键问题是如何为大规模生物网络开发创新的网络过滤器,利用并行算法构建网络采样器,在发现新结构的同时保留原始网络结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HPCS 2012 tutorials: Tutorial I: High performance computing in biomedical informatics
The last decade has witnessed significant developments in various aspects of Biomedical Informatics, including Bioinformatics, Medical Informatics, Public Health Informatics, and Biomedical Imaging. The explosion of medical and biological data requires an associated increase in the scale and sophistication of the automated systems and intelligent tools to enable the researchers to take full advantage of the available databases. The availability of vast amount of biological data continues to represent unlimited opportunities as well as great challenges in biomedical research. Developing innovative data mining techniques and clever parallel computational methods to implement them will surely play an important role in efficiently extracting useful knowledge from the raw data currently available. The proper integration of carefully selected/developed algorithms along with efficient utilization of High Performance Computing (HPC) systems form the key ingredients in the process of reaching new discoveries from biological data. This tutorial focuses on addressing several key issues related to the effective utilization of HPC in biomedical informatics research, in particular, how to efficiently utilize high performance systems in the analysis of massive biological data. A major issue is that regard is how to design energy-aware models for executing computationally-intensive biomedical applications on HPC systems. Another key issue is how to develop innovative network filters for massive biological networks that would utilize parallel algorithms to construct networks samplers that would preserve original network structures while uncovering new ones.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信