Circulating nucleic acids have been shown to have potential as non-invasive diagnostic markers in cancer. We therefore investigated whether microRNAs also have diagnostic utility by comparing levels of tumour-associated MIRN155 (miR-155), MIRN210 (miR-210) and MIRN21 (miR-21) in serum from diffuse large B-cell lymphoma (DLBCL) patients (n = 60) with healthy controls (n = 43). Levels were higher...

Contemporary statistical research frequently deals with problems involving a diverging number of parameters. For those problems, various shrinkage methods (e.g., LASSO, SCAD, etc) are found particularly useful for the purpose of variable selection (Fan and Peng, 2004; Huang et al., 2007b). Nevertheless, the desirable performances of those shrinkage methods heavily hinge on an appropriate select...

Processing of miRNAs occurs simultaneous with the transcription and splicing of their primary transcripts. For the small subset of exonic miRNAs it is unclear if the unspliced and/or spliced transcripts are used for miRNA biogenesis. We assessed endogenous levels and cellular location of primary transcripts of three exonic miRNAs. The ratio between unspliced and spliced transcripts varied marke...

Two methods for clustering data and choosing a mixture model are proposed. First, we derive a new classification algorithm based on the classification likelihood. Then, the likelihood conditional on these clusters is written as the product of likelihoods of each cluster, and AICrespectively BIC-type approximations are applied. The resulting criteria turn out to be the sum of the AIC or BIC rela...

Block clustering (or co-clustering or simultaneous clustering) aims at simultaneously partitioning the rows and columns of a data table to reveal homogeneous block structures. This structure can stem from the latent block model which provides a probabilistic modelling of data tables whose blocks arise from row and column clusters. For continuous data, each table entry is typically assumed to fo...

SUMMARY We propose a beta-mixture model approach to solve a variety of problems related to correlations of gene-expression levels. For example, in meta-analyses of microarray gene-expression datasets, a threshold value of correlation coefficients for gene-expression levels is used to decide whether gene-expression levels are strongly correlated across studies. Ad hoc threshold values such as 0....

This paper concerns the asymptotic properties of a class of criteria for model selection in linear regression models, which covers the most well known criteria as e.g. MALLOWS' Cp, CV (cross-validation), GCV ( generalized cross-validation), AKAIKE's AIC and FPE as well as SCHWARZ' BIC. These criteria are shown to be consistent in the sense of selecting the true or larger models, assuming i.i.d....

Penalized regression methods offer an attractive alternative to single marker testing in genetic association analysis. Penalized regression methods shrink down to zero the coefficient of markers that have little apparent effect on the trait of interest, resulting in a parsimonious subset of what we hope are true pertinent predictors. Here we explore the performance of penalization in selecting ...

MicroRNAs are a class of small RNAs that are increasingly being recognized as important regulators of gene expression. Although hundreds of microRNAs are present in the mammalian genome, genetic studies addressing their physiological roles are at an early stage. We have shown that mice deficient for bic/microRNA-155 are immunodeficient and display increased lung airway remodeling. We demonstrat...

SUMMARY Based on the marginal likelihood approach, we develop a model selection criterion, MIC, for regression models with the general variance structure. These include weighted regression models, regression models with ARMA errors, growth curve models, and spatial correlation models. We show that MIC is a consistent criterion. For regression models with either constant or non-constant variance...