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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).
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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.
We evaluated the methods on 13 modified DUD data sets covering a wide range of different molecules and chemotypes.
We evaluated the methods on simulated data sets, a data set with affinity data against a large fraction of the human kinome, and four smaller subsets of the aforementioned kinome data.
Thus we are only evaluating the methods on their ability to find a correct CAL during the genome-wide global alignment, and not their ability to finish this with correct variant detection.
Furthermore, we evaluate the methods' dependence on the number of source components retained.
The paper demonstrates and evaluates the method on a safety-critical product-line subsystem, the Door Control System.
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