Parallelizing the execution of native data mining algorithms for computational biology

摘要

Data mining is being increasingly used in biology. Biologists are adopting prototyping languages, like Rrnand Matlab, to facilitate the application of data mining algorithms to their data. As a result, their scripts arernbecoming increasingly complex and also require frequent updates. Application to large datasets becomesrnimpractical and the time-to-paper increases. Furthermore, even if there are various systems that can be usedrnto efficiently process large datasets, for example, using Cloud and High Performance Computing, they usuallyrnrequire procedures to be translated into specific languages or to be adapted to a certain computingrnplatform. Such modifications can speed up the processing, but translation is not automatic, especially inrncomplex cases, and can require a large amount of programming effort and accurate validation. In this paper,rnwe propose an approach to parallelize data mining procedures in the form of compiled software or R scriptsrndeveloped by biology communities of practice. Our approach requires minimal alteration of the originalrncode. In many cases, there is no need for code modification. Furthermore, it allows for fast updating whenrna new version is ready. We clarify the constraints and the benefits of our method and report a practical userncase to demonstrate such benefits compared with a standard execution. Our approach relies on a distributedrnnetwork of web services and ultimately exposes the algorithms as-a-Service, to be invoked by remote thinrnclients.