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These methods usually take different names depending on the type of considered distance.
Supervised classification methods usually take a number of labeled samples to train the classifiers, and the classification accuracy depends on the selected training samples [2].
Existing methods usually take advantage of the hierarchical structure of Gene Ontology to reduce redundancy, a solution that is only effective for GO or other hierarchically organized gene sets and not applicable to many others, such as pathways, experimental signatures and regulator targets.
Agglomerative clustering methods usually take an input of pairwise similarities among data items, from which cluster similarities are then inferred.
However, based on gel electrophoresis, these methods usually take long time and high labor costs with large samples size.
It is important to emphasize that the classical standard methods usually take "all the exons" (i.e. they follow the first approach named here ESLiM-all), and so, this is the by-default approach that does not consider the architecture of the gene loci.
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These methods usually took a long time to compute the relation between input images in order to acquire a large amount of cloud points with high accuracy.
The higher generalization of the method usually takes for granted the strong convergence of the solution over larger spatial and parameter domains.
In routine clinical practice, early anti-infection treatment should be given before definitive diagnosis since blood culture, the gold-standard diagnostic method, usually takes several days to obtain the results and frequently yields low positive results.
The conventional DST method usually took longer than two months to execute [ 12], so it could prolong the diagnosis and treatment of MDR pathogen.
This method usually takes 5 business days.[10].[10]
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