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We evaluate the method on yield data of two semiconductor products and describe possible directions for the future use of automated perceptions in data mining and knowledge discovery.
We discuss the formulation of InVEst, and evaluate the method on simulated data.
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To this end, we adopt the following strategy used in Singh-Blom et al. (2013): We look at singleton genes, i.e. genes that have only one association in the data, and evaluate the methods on how highly the corresponding gene associations are ranked.
Thus, it is crucial to evaluate the methods on which a guideline is developed [17, 18].
Nevertheless, clinical guidelines may vary widely in quality; as a consequence, it is important to evaluate the methods on which a guideline was developed in order to be confident with its recommendations [13, 14].
Then, we evaluate the methods on the associations recently reported in the literature, collected by Börnigen et al. (2012), and the associations recorded in the OMIM database between August 2011 and November 2013 (Section 4.7).
The paper demonstrates and evaluates the method on a safety-critical product-line subsystem, the Door Control System.
We evaluated the method on a large, highly cluttered data set of a building with forty separate rooms.
We evaluated the method on simulated and in vivo human brain data, comparing the results with the traditional single-tensor and with a probabilistic tractography technique.
We evaluated the method on a benchmark composed of target secondary structures extracted from the RNA STRAND database.
We also validate the novelty of predictions by evaluating the method on recently reported OMIM associations and on associations recently reported in the literature.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
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CEO of Professional Science Editing for Scientists @ prosciediting.com