Journal Article10.1080/10485250802613558
A Bayesian nonparametric method for model evaluation: application to genetic studies
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TL;DR: In this paper, a nonparametric Bayesian method for model assessment was proposed for gene-disease relationship, and the authors demonstrate the advantages of this approach particularly when the sample size is small and/or the true model is non-linear.
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Abstract: Statistical models applied to genetic studies commonly assume linear relationships (between disease and risk factors) and simple distributional forms (by relying on asymptotic methods) for inference. However, when the sample size is small, inference using traditional asymptotic models can be problematic. Moreover, the gene-disease relationship is not always linear. In this article, we present a new nonparametric Bayesian method for model assessment, and we demonstrate the advantages of this approach particularly when the sample size is small and/or the true model is non-linear. We evaluate our approach on simulated data and find that it performs substantially better than alternative models. We also apply our method to two real studies: diagnosis of conventional high-grade non-metastatic osteosarcoma, and survival in Burkitt's lymphoma.
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Mathematical modeling of molecular data in translational medicine: theoretical considerations
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TL;DR: A new classification method based on Dirichlet process mixtures to investigate the classification of the four actions from the action surface EMG(ASEMG) signals and the results indicate that this classification method could be applied the Classification of the ASEMG signals.
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