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This system is based on linear workflow.
Our approach to workflow model management is based on a structured workflow lifecycle and leverages recent advances in model management and case-based reasoning techniques.
Specifically, HealthFlow: Is shareable, because it is based on a cross-industry workflow standard.
The type of analysis performed is based on the analysis workflow selected.
It is based on the galaxy workflow engine and provides users with an interface to create and track sequencing requests.
It is based on the new workflow data pattern called self-adaptive multiple instances (P-SaMIs) and on a middleware built on Amazon EC2 instances.
It is based on the new workflow data pattern called Self-adaptive Multiple Instandes (P-SaMI) and on a middleware built on Amazon EC2 instances.
An alternative structural approach which we use to represent each workflow is based on graphs as basic specifications for workflow structures.
It is based on our recently published workflow for the detection of metabolic features derived from native and fully labeled biological samples, which has been further developed to support fast polarity switching and automated annotation of metabolic features of the detected metabolites.
The work is based on a fairly straightforward workflow involving protein pull-down studies, protein identification by mass spectrometry, structure-function analysis to define regions and residues that interact, then a more functional set of studies where G to F-actin dynamics are manipulated in cells.
Our approach is based on capturing one-level data provenance at each transformation when the workflow is run initially.
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