Abstract
Scientific workflows are becoming more popular in the research community, due to their ease of creation and use, and because of the benefits of repeatability of such workflows. In this paper we investigate the benefits of workflows in a genomics experiment which requires intensive computing as well as parallelization, and show that substantial optimizations in rule redundancy reduction can be achieved by simple workflow parallelization. © 2010 Springer-Verlag.
Cite
CITATION STYLE
De Bruin, J. S., & Kok, J. N. (2010). Combining subgroup discovery and permutation testing to reduce reduncancy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6415 LNCS, pp. 285–300). https://doi.org/10.1007/978-3-642-16558-0_25
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