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If one can establish a method to correctly predict the target groups of a query drug, the possible target proteins would be limited only to the predicted group, facilitating further analyses.
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A tree-based model was also used as an alternative method to correctly classify patients and controls.
The purpose of this study is to develop a novel segmentation method to correctly separate the fat flecks from the muscle in the rib-eye region in a beef image and to compare the proposed segmentation method to prior algorithms.
The failure of standard methods to "correctly" align a set of residues known to be highly conserved among these structure-function homologs prompted us to re-examine protein structure comparison and alignment criteria in non-structure core regions.
In view of this, here we will try to devise an effective method to correctly recognize the possible activity prediction of small molecules based on physical and chemical properties of the compounds.
Specifically, the true positive rate refers to the ability of the method to correctly identify a specific gesture, whilst the false positive rate refers to the percentage of gestures wrongly classified.
The ability of the method to correctly adjust for batch effects is further illustrated in Figure 3.
However, for a method to execute correctly, the generated input parameters must not violate implicit semantical constraints, such as ranges of numeric parameters or the maximum length of a collection.
This affects the ability of phylogenetic methods to correctly estimate phylogenetic trees, especially for long timescales.
Given that the NHANES sample was nonrandom, we used the recommended methods to correctly estimate variances (NCHS 2007).
This method allows to correctly evaluate descriptor efficiency.
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