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

现代生物医学研究大量使用大型数据集。云计算通过按需提供虚拟计算机和存储磁盘来高效地存储和处理海量数据，而不需要大量的前期成本，从而成为一种经济高效的解决方案。尽管取得了一些进展，但云计算在生物医学研究中的应用仍处于非常早期的阶段。对于如何最好地利用云来加速大规模的生物医学应用，存在着各种各样的担忧。特别是，生物医学应用程序可以不需要任何修改就直接迁移到云上吗？如何开发基于云的生物医学应用程序？云中的应用程序的性能和成本是多少？性能和成本可以接受吗？我们是否有最优的方法将应用程序的性能和总体成本保持在云中可接受的范围内？本项目将开发一种云方法，用于实际的生物医学数据密集型任务，通过解决上述问题来有效地对大规模图像数据进行基因表达模式识别和注释。选择这项任务在很大程度上是因为它在生物医学研究中的重要性。这种密集的数据分析任务在生物医学科学中越来越常见。这项特殊的任务涉及到小鼠胚胎的发育解剖学：通过解剖学注释来识别与胚胎发育和生理功能相关的基因相互作用和网络是非常有意义的。基因表达模式识别和注释代表了标记胚胎图像的解剖学术语，用于小鼠的发育。如果图像被标记了一个术语，这意味着相应的解剖组件显示了该基因的表达。目前，这项任务主要由领域专家手动完成。然而，随着海量数据的可用，手动批注既昂贵又耗时。此外，人工注释还可能产生由人工注释员引入的跨图像的标签不一致，因为它被证明是高度主观的。为了缓解手动注释的问题，我们采用了数据挖掘技术来自动识别胚胎图像中的解剖成分，并使用提供的术语对图像进行注释。由于这项任务涉及到超大规模图像的使用，我们打算利用云计算来解决这一任务中的海量数据问题，预计该项目的成功完成将为访问和利用云计算技术来分析大规模基于图像的生物医学数据提供一个典型的范例。这项提案的一个重要且新颖的方面是，限制云计算在生物医学应用中更广泛使用的主要担忧将得到解决。这项工作的理论部分旨在提供(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；(2)一套标准服务(如图像处理算法、数据挖掘算法)和一种新颖的自动数据重用机制，以提高性能和降低成本，可重用并插入类似的生物医学应用类别。

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.