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We recently developed a novel machine learning technique, the robust support vector machine (rSVM), that can achieve highly accurate classification in the face of data uncertainty from noise in training data, ubiquitous in clinical research.[1] Methods: We used the rSVM to classify participants as having manifest/nonmanifest HD based on UHDRS motor subscores, with observerrated DCL classifications forming the reference standard

In humans, circulating ghrelin levels heightened in anorexia nervosa and during sustained fasting with weight loss 70,71 and are lowered in acute states of positive energy balance and in obesity

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