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BACKGROUND Activation of free fatty acid receptor 1 (FFAR1; also known as G-protein-coupled receptor 40) by fatty acids stimulated glucose-dependent β-cell insulin secretion in preclinical models. We aimed to assess whether selective pharmacological activation of this receptor by TAK-875 in patients with type 2 diabetes mellitus improved glycaemic control(More)
Clustering analysis is one of the most widely used statistical tools in many emerging areas such as microarray data analysis. For microarray and other high-dimensional data, the presence of many noise variables may mask underlying clustering structures. Hence removing noise variables via variable selection is necessary. For simultaneous variable selection(More)
We consider penalized linear regression, especially for "large p, small n" problems, for which the relationships among predictors are described a priori by a network. A class of motivating examples includes modeling a phenotype through gene expression profiles while accounting for coordinated functioning of genes in the form of biological pathways or(More)
Penalized model-based clustering has been proposed for high-dimensional but small sample-sized data, such as arising from genomic studies; in particular, it can be used for variable selection. A new regularization scheme is proposed to group together multiple parameters of the same variable across clusters, which is shown both analytically and numerically(More)
MOTIVATION Model-based clustering has been widely used, e.g. in microarray data analysis. Since for high-dimensional data variable selection is necessary, several penalized model-based clustering methods have been proposed tørealize simultaneous variable selection and clustering. However, the existing methods all assume that the variables are independent(More)
We explored approaches to using multiple related traits (gene expression levels) in linkage analysis. We first grouped mRNA transcripts according to their functions annotated in biological process of gene ontology (GO). We then compared using sample average, principal-components analysis (PCA), and linear discriminant analysis (LDA) to derive a univariate(More)
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