Parallel and Cloud-Based Analysis of Omics Data: Modelling and Simulation in Medicine
Parallel and Cloud-Based Analysis of Omics Data: Modelling and Simulation in Medicine
复制标题
组学数据的并行和基于云的分析：医学建模和模拟
DOI：
10.1109/pdp.2017.68
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
M. Cannataro
中科院分区：
文献类型：
--
作者：
Giuseppe Agapito;B. Calabrese;P. Guzzi;G. Fragomeni;G. Tradigo;P. Veltri;M. Cannataro
临床环境和研究实验室中可用的高通量实验平台和诊断设备，如磁共振成像、微阵列、质谱和下一代测序，正在产生越来越多的临床和组学数据。此外，电子病历（EPR）、电子健康系统、个人移动的传感器和社交网络正在收集大量的健康和生活方式数据，这些数据可以与临床数据集成，并且越来越多地用于实时监测患者的健康。这在安全数据存储、数据集成的有效模型、数据分析的有效算法、健康监测的新模型方面提出了新的问题，这些问题可以使用高性能计算解决方案来解决。并行计算和云计算可以以正交的方式提供高效和可扩展的解决方案。事实上，利用现成的高性能计算机的并行生物信息学软件可以用于在较低层预处理和分析组学数据，例如突出与复杂疾病相关的遗传变异。另一方面，云计算提供大规模的数据存储、数据共享服务、随时随地按需访问资源和应用，实现弹性和可扩展的应用和服务。受并行计算和云计算在生命科学中越来越多的使用的启发，本文中，我们调查了用于组学数据的并行预处理和统计与数据挖掘分析的并行生物信息学算法，以及用于大规模应用的基于云的医疗保健和生物医学服务和系统。此外，该文件强调了与使用此类平台存储和分析健康数据有关的主要问题，特别关注患者数据的安全性和隐私性，这在个性化医疗等领域尤为重要。最后给出了医学和生物学中并行分布式建模与仿真的应用实例。
High throughput experimental platforms and diagnostic equipments available in clinical settings and in research laboratories, such as magnetic resonance imaging, microarray, mass spectrometry and next-generation sequencing, are producing an increasing volume of clinical and omics data. Moreover, Electronic Patients Records (EPRs), eHealth systems, personal mobile sensors and Social Networks are collecting an overwhelming volume of health and life style data that may be integrated with clinical data and more and more is used for the real-time monitoring of patient's health. This poses new issues in terms of secure data storage, effective models for data integration, efficient algorithms for data analysis, new models for health monitoring, that may be addressed, among the others, using high performance computing solutions. Parallel computing and Cloud Computing may offer efficient and scalable solutions in an orthogonal way. In fact, parallel, bioinformatics software, that exploit off-the-shelf high performance computers, may be used to preprocess and analyze omics data at a lower layer, for instance to highlight genetic variation associated with complex diseases. On the other hand, Cloud Computing offers large scale data storage, data sharing services, on-demand anytime and anywhere access to resources and applications, for the realization of elastic and scalable applications and services. Motivated by the increasing use of parallel computing and cloud computing in life sciences, in this paper we survey both parallel bioinformatics algorithms for the parallel preprocessing and statistical and data mining analysis of omics data, as well as Cloud-based healthcare and biomedicine services and systems for large scale applications. Moreover, the paper underlines main issues and problems related to the use of such platforms for the storage and analysis of health data, with special focus to the security and privacy of patients data, that are particularly important in fields such as personalized medicine. Finally, the paper presents some case studies about the parallel and distributed modelling and simulation in medicine and biology.
DOI：
--
发表时间：
--
期刊：
--
影响因子：
--
作者：
Systems Biology
通讯作者：
Systems Biology