AGILE: A Cloud Approach to Automatic Gene Expression Pattern Recognition and Annotation Over Large-Scale Images

AGILE：大规模图像上自动基因表达模式识别和注释的云方法

基本信息

批准号：
BB/K004077/1
负责人：
Liangxiu Han
金额：
$ 14.1万
依托单位：
Manchester Metropolitan University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2012
资助国家：
英国
起止时间：
2012 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FK004077%2F1
关键词：
AGILE Cloud Approach Automatic Gene

项目摘要

Modern biomedical research makes significant use of large datasets. Cloud computing is emerging as a cost-effective solution by providing virtual computers and storage disks on demand to store and process massive data efficiently without large upfront costs.Despite some progress made, the use of cloud computing in the biomedical research is still at the very early stage. There exist various concerns on how to best utilise the cloud for accelerating large-scale biomedical applications. Especially, can a biomedical application be directly migrated to the cloud without requiring any modification? How to develop a cloud-based biomedical application? What are the performance and the cost of an application in the cloud? Are the performance and the cost acceptable? Do we have optimal methods to keep both performance and the overall cost of applications within the acceptable range in the cloud?This project will develop a cloud approach for a real biomedical data intensive task for effective gene expression pattern recognition and annotation over large-scale image data through addressing the concerns above. This task is chosen largely for its importance in the biomedical research. This type of intensive data-analysis task is increasingly common in the biomedical sciences. This particular task concerns developmental anatomy of mouse embryo: it is of great interest to identify gene interactions and networks that are associated with developmental and physiological functions in the embryo by using anatomical annotation. The gene expression pattern recognition and annotation represents labelling embryo images with anatomical terms for mouse development. If an image is tagged with a term, it means the corresponding anatomical component shows expression of that gene. Currently, this task is mainly taken manually by domain experts. However, with the availability of the vast amount of data, a manual annotation is expensive and time consuming. Additionally, the manual annotation may also produce the inconsistency of labels across images introduced by the human annotators as it proves to be highly subjective. To alleviate issues with the manual annotation, we have employed data mining techniques to automatically identify an anatomical component in the embryo image and annotate the image using the provided terms. As this task involves the use of very large-scale images, we intend to exploit cloud computing for this task to address the massive data problems.It is expected that the successful completion of this project will provide a typical exemplar for accessing and exploiting cloud computing technologies to analyse large-scale image-based biomedical data. An important, and novel, aspect of this proposal is that the major concerns that limit the more widespread use of cloud computing for biomedical applications will be addressed. The theoretical component of the work aims to provide (1) a practical user-friendly biomedical data-mining tool based on the cloud for effective gene expression pattern recognition and annotation and (2) a set of standard services (e.g. image processing algorithms, data mining algorithms) and a novel automatic data reuse mechanism for performance enhancement and cost reduction, which can be reused and plugged into the class of similar biomedical applications.

现代生物医学研究大量使用了大型数据集。通过提供虚拟计算机和存储磁盘的需求，可以有效地存储和处理大量数据，而无需大量的前期成本，云计算正在作为一种具有成本效益的解决方案。关于如何最好地利用云来加速大规模生物医学应用，存在各种担忧。特别是，生物医学应用程序可以直接迁移到云中而无需任何修改吗？如何开发基于云的生物医学应用？云中应用程序的性能和成本是多少？性能和成本可以接受吗？我们是否有最佳方法可以在云中可接受的范围内保持性能和应用程序的整体成本？该项目将开发一种云方法，用于通过解决上述问题来实现实际生物医学数据密集型任务，以实现有效的基因表达模式识别和对大规模图像数据的注释。此任务主要是因为其在生物医学研究中的重要性。在生物医学科学中，这种类型的密集数据分析任务越来越普遍。这项特殊的任务涉及小鼠胚胎的发育解剖结构：通过使用解剖学注释，确定与胚胎中与胚胎中发育和生理功能相关的基因相互作用和网络引起的极大兴趣。基因表达模式识别和注释表示具有小鼠发育的解剖学术语的胚胎图像。如果图像用术语标记，则表示相应的解剖分量显示该基因的表达。目前，此任务主要由域专家手动进行。但是，随着大量数据的可用性，手动注释是昂贵且耗时的。此外，手动注释还可能在人类注释者引入的图像上产生标签的不一致，因为它被证明是高度主观的。为了减轻手动注释的问题，我们采用了数据挖掘技术来自动识别胚胎图像中的解剖组分，并使用提供的术语来注释图像。由于此任务涉及使用非常大规模的图像，我们打算利用云计算来解决此任务，以解决大规模的数据问题。预计，该项目的成功完成将为访问和利用云计算技术提供典型的典范，以分析大型图像基于图像的基于图像的生物医学数据。该提案的一个重要且新颖的方面是，将解决限制云计算在生物医学应用中更广泛使用的主要问题。工作的理论组成部分旨在提供（1）基于云的实用用户友好的生物医学数据挖掘工具，用于有效的基因表达模式识别和注释，以及（2）一组标准服务（例如，图像处理算法，数据挖掘算法）以及可重复使用的自动数据挖掘和相似的成本机制，以增强和启动性能，以增强性能和相似的成本，以增强和重新降低，以使其适用于型号，以增强和重新降低，并将其重新降低，以使其适用于重还原，并将其重新降低。申请。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Automatic data reuse for accelerating data intensive applications in the Cloud

DOI：
10.1109/icitst.2013.6750272
发表时间：
2013-12
期刊：
8th International Conference for Internet Technology and Secured Transactions (ICITST-2013)
影响因子：
0
作者：
Liangxiu Han;Zheng Xie;R. Baldock
通讯作者：
Liangxiu Han;Zheng Xie;R. Baldock

A genetic algorithm enhanced automatic data flow management solution for facilitating data intensive applications in the cloud