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Machine learning methods are alternatives to traditional statistical approaches.
These in vitro digestion methods are alternatives to in vivo methods due to their low cost, energy saving properties, and time and their independence from physiological factors [ 15, 16].
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Thresholding methods are alternative approaches to the denoising problem.
Rule- and template-based pattern-recognition methods are alternative ways to identify various patterns in stock prices alongside more traditional econometric tools.
Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins.
Besides the biotechnology experiments, in silico prediction methods are alternative ways to identify the potential hydroxylation sites.
On the other hand, network-based modeling methods are alternative and complementary strategies to assess enzymatic inhibitions.
These approaches might provide insights and intuitively simple static methods are alternative in small experiments in general.
The method is alternative to the classical isopiestic approach and offers the advantage of a continuous record on increasing the solute concentration.
Global optimization methods that are alternative to evolutionary approaches are reviewed.
Semi-automated and automated segmentation methods are favourable alternatives by reducing variability in delineation and decreasing the contouring time [20].
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