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Results show that the glmBoost + GBM model demonstrated outstanding and stable predictive performance, achieving the highest area under the curve (AUC) on the training set (AUC = 0.987) and maintaining robust accuracy in external validation (AUCs of 0.826 and 0.721, respectively)
Researchers characterized the metabolic profiles of three amylin receptor agonists, identified metabolites suitable for anti-doping analysis, and developed and validated an approach for detection in anti-doping laboratories
Pros: Highest convenience, mimics natural physiology best
Reducing glucagon secretion: Prevents excessive glucose production by the liver
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