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The Colorectal Cancer (CRC) is one of the most frequent causes of death from cancer in the developed regions. In this paper we address the necessity to develop tools by which the oncologists will easily identify molecular targets for treatment and perform immediate diagnosis. Considering the increase of available microarray experiments and the DNA chips’ potentiality for extracting knowledge from the genes’ expressions, we present the design of a new platform for CRC gene expression analysis whose usage target are both the researchers and the medical persons. The platform is set on predefined and original statistical methodologies for accurate inference in CRC, biomarkers extraction, CRC stage prognosis, etc. In order to avoid the limitations of the local infrastructure, the proposed architecture takes the advantage of the cloud's speed and flexibility to perform large-scale computations and to be easily upgradable with new tools. The platform we present is equivalent to the Software as a Service level of abstraction from an end-user point of view. From a developer's point of view, we use the Platform as a Service level of abstraction, which means we control virtualized instances that are already offered by the Cloud Service Provider. Those instances are set on the Infrastructure as a Service level of the cloud, its storing and processing capacity. Mainly, the platform is comprised of four distinctive modules: User Interface – interface between the user, the software and the biological databases; Machine Learning – the core where the data is processed; Distributed Processing – splits and spreads the problem through the available instances; and Resources Controlling module – determines the best combination of resources in terms of performance and cost depending on the problem size. A detailed description of each module, as well as the latest concerns of cloud's interoperability and portability is discussed in the paper.

Platform's Architecture for Colorectal Cancer Research in the Cloud