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Methods for discriminating outcomes will impact clinical trials and understanding disease pathophysiology.
The introduction of affordable microarray technologies for measuring genome-wide expression profiles has led to the development of numerous methods for discriminating between different classes of a complex disease, such as cancer, through transcriptome analysis [1] [4].
Diagnostic methods for discriminating cancers of the pancreatic head and the distal bile duct often overlap.
Numerous genetic methods for discriminating H. influenzae from other species have been described [ 16– 23].
Nevertheless, in a future study, we intend to investigate the performance of machine learning methods for discriminating perimetric and preperimetric glaucoma.
Many researchers have carried out comparisons between RFLP and 12 or 15 loci VNTR typing methods for discriminating M. tuberculosis isolates [ 3, 4, 12].
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In this application, we present an assessment of the proposed discriminant clustering method for discriminating healthy people from people with PD by detecting dysphonia.
A method for discriminating between measurements made on sound and damaged structures is presented.
The result is an efficient method for discriminating rival models and extracting active site densities and adsorption-reaction rate constants from transient response data.
Thus, developing a reliable method for discriminating H. niger seeds from its adulterants is necessary to reduce confusion and ensure the safe use of Hyoscyami Semen.
The challenge here is to design a method for discriminating correct matches from unreliable matches between ground-view images and an aerial image.
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