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Resampling techniques, based on the intensive computational capacity of computers, have been widely applied to PopPK modeling for the assessment of uncertainty in parameter estimations and the detection of influential observations [ 9– 12].
Launching of a large number (~10) of photon packets and computing their interaction with medium and with the probe is a highly intensive computational process.
On its own, training a machine learning model is already an incredibly intensive computational process.
Urban flood risk modelling is a highly topical example of intensive computational processing.
However, due to the intensive computational requirements of ensemble forecasts, the spatial resolution remains coarse.
Solution of large order fluid structure interaction (FSI) systems with parametric uncertainties demand intensive computational resources.
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Examples include integrated clients for data-mining services or simple lookup services or integrated APIs for submitting data-intensive computational jobs on different types of compute resources.
While Google and Yahoo use this technique for searching the Web, it can be used for any data-intensive computational problem.
We examine how data-intensive computational systems originally built for cosmology, the Sloan Digital Sky Survey (SDSS), are now being used in connectomics, at the Open Connectome Project.
Because experimental methods used for identifying catalytic residues are resource- and labor-intensive, computational approaches have considerable value and are highly desirable for their ability to complement experimental studies in identifying catalytic residues and helping to bridge the sequence structure function gap.
Orione is based on Galaxy (Goecks et al., 2010), an open platform for reproducible data-intensive computational analysis used in many diverse biomedical research environment.
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