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Cebus albifrons monkeys were trained to escape electrical stimulation of either leg at five intensities, spanning a range from mild tingle to intense but tolerable pain, as judged by human observers who experienced the same stimuli. The average duration of stimulation received by the animals at each intensity was plotted for each leg during the period(More)
Long-term endurance exercise severely affects metabolism in both human and animal athletes resulting in serious risk of metabolic disorders during or after competition. Young horses (up to 6 years old) can compete in races up to 90 km despite limited scientific knowledge of energetic metabolism responses to long distance exercise in these animals. The(More)
Metabolic profiling, the study of changes in the concentration of the metabolites in the organism induced by biological differences within subpopulations, has to deal with a very large amount of complex data. It therefore requires the use of powerful data processing and machine learning methods. To overcome over-fitting, a common concern in metabolic(More)
1H Nuclear Magnetic Resonance (NMR)-based metabolic profiling is very promising for the diagnostic of the stages of chronic kidney disease (CKD). Because of the high dimension of NMR spectra datasets and the complex mixture of metabolites in biological samples, the identification of discriminant biomarkers of a disease is challenging. None of the widely(More)
Multivariate classification methods using explanatory and predictive models are necessary for characterizing subgroups of patients according to their risk profiles. Popular methods include logistic regression and classification trees with performances that vary according to the nature and the characteristics of the dataset. In the context of imported(More)
This study proposed an exhaustive stable/reproducible rule-mining algorithm combined to a classifier to generate both accurate and interpretable models. Our method first extracts rules (i.e., a conjunction of conditions about the values of a small number of input features) with our exhaustive rule-mining algorithm, then constructs a new feature space based(More)
An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients. In this paper, we propose a deep learning method that directly models the survival function instead of estimating the hazard function to predict survival times for graft patients based on the principle(More)
Any biochemical reaction underlying drug metabolism depends on individual gene-drug interactions and on groups of genes interacting together. Based on a high-throughput genetic approach, we sought to identify a set of covariant single-nucleotide polymorphisms predictive of interindividual tacrolimus (Tac) dose requirement variability. Tac blood(More)
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