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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.
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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.
The paper demonstrates and evaluates the method on a safety-critical product-line subsystem, the Door Control System.
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 also validate the novelty of predictions by evaluating the method on recently reported OMIM associations and on associations recently reported in the literature.
We also validate the novelty of predictions by evaluating the method on recently discovered gene associations recorded in the OMIM database, as well as on associations recently reported in the literature curated by Börnigen et al. (2012).
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.
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