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However, none of these approaches has achieved significant popularity, perhaps because they lack the conceptual simplicity of the PWM.
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Recently deep learning approaches have achieved more attention for recognition of species or individual animal using visual features.
In recent years, learning features from unlabeled data using unsupervised feature learning and deep learning approaches have achieved superior performance in solving many computer vision problems [22 25].
Although machine learning-based approaches have achieved the highest results, some rule-based and hybrid systems [22, 33] showed highly competitive results during the BioCreative Challenge.
These approaches have achieved very good to excellent accuracy when compared to expert manual tissue segmentations of cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM).
To detect positive selection, two major approaches have achieved significant success.
These approaches have achieved silencing of endogenous genes and specific loss-of-function phenotypes in transgenic mice.
These approaches have achieved some success and demonstrated superior bioactivity when compared to fully synthetic scaffolds [ 10, 11].
Some combination approaches have achieved enhanced antitumor effects in animal models and human clinical studies, 29, 30, 31 in which monotherapy has exerted only modest effects.
While some face-to-face approaches have achieved promising outcomes, they are costly and resource-intensive to provide, restricting their reach and penetration.
These approaches have achieved successful outcomes; however, unlike a cohort study, data from control subjects may not reflect the true distribution of ages for the target population.
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