Background: Gastric cancer (GC) is one of the most prevalent cancers with a high rate of mortality in the world. In recent years, microRNAs (miRNAs) have been proposed to be involved in GC development. In this study, we aimed at investigating differential expression level of miR-155-5p, miR-15a, miR-15b, and miR-186 in GC. Methods: For this research, we used qPCR to investigate miR-15b, miR-155...

Analysis of health care cost data is often complicated by a high level of skewness, heteroscedastic variances and the presence of missing data. Most of the existing literature on cost data analysis have been focused on modeling the conditional mean. In this paper, we study a weighted quantile regression approach for estimating the conditional quantiles health care cost data with missing covaria...

Dispersal can impact population dynamics and geographic variation, and thus, genetic approaches that can establish which landscape factors influence population connectivity have ecological and evolutionary importance. Mixed models that account for the error structure of pairwise datasets are increasingly used to compare models relating genetic differentiation to pairwise measures of landscape r...

The estimation of the correct number of dimensions is a long-standing problem in psychometrics. Several methods have been proposed, such as parallel analysis (PA), Kaiser-Guttman's eigenvalue-greater-than-one rule, multiple average partial procedure (MAP), the maximum-likelihood approaches that use fit indexes as BIC and EBIC and the less used and studied approach called very simple structure (...

Numerous computational methods exist to assess the mode and strength of natural selection in protein-coding sequences, yet how distinct methods relate to one another remains largely unknown. Here, we elucidate the relationship between two widely used phylogenetic modeling frameworks: dN/dS models and mutation-selection (MutSel) models. We derive a mathematical relationship between dN/dS and sca...

Model-based clustering consists of fitting a mixture model to data and identifying each cluster with one of its components. Multivariate normal distributions are typically used. The number of clusters is usually determined from the data, often using BIC. In practice, however, individual clusters can be poorly fitted by Gaussian distributions, and in that case model-based clustering tends to rep...

When a number of models are fit to the same data set, one method of choosing the 'best' model is to select the model for which Akaike's information criterion (AIC) is lowest. AIC applies when maximum likelihood is used to estimate the unknown parameters in the model. The value of -2 log likelihood for each model fit is penalized by adding twice the number of estimated parameters. The number of ...

Penalized selection criteria like AIC or BIC are among the most popular methods for variable selection. Their theoretical properties have been studied intensively and are well understood, but making use of them in case of high-dimensional data is difficult due to the non-convex optimization problem induced by L0 penalties. In this paper we introduce an adaptive ridge procedure (AR), where itera...

For a given gene, the total number of potential predictors p in BBSR is determined by the size of the union of the 10 highest scoring predictors based on tlCLR and all those predictors that have previously been reported as regulators (see Method section in main article). If p is large (> 10) it becomes infeasible to compute all 2 possible regression models during the model selection step. To fu...