Mixture models for detecting differentially expressed genes in microarrays

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local FDR (false discovery rate) is provided for each gene. An attractive feature of the mixture model approach is that it provides a framework for the estimation of the prior probability that a gene is not differentially expressed, and this probability can subsequently be used in forming a decision rule. The rule can also be formed to take the false negative rate into account. We apply this approach to a well-known publicly available data set on breast cancer, and discuss our findings with reference to other approaches.

Thin-layer isothermal drying of fructose, maltodextrin, and their mixture solutions

Solutions of fructose, maltodextrin (DE 5), and their mixtures at the ratios of 20:80, 40:60, 50:50, 60:40, and 80:20 were gelled with 1% agar-agar and dried under convective-conductive drying conditions. The thin slabs were maintained at isothermal drying condition of 30 and 50 degrees C. Yamamoto's simplified method based on regular regime approach was used to calculate the (effective) moisture diffusivity. Both the drying rates and the moisture diffusivity exhibited strong concentration dependence. The concentration dependence was stronger in the case of fructose and fructose rich solutions. Both the moisture diffusivity and drying rates of the mixture solutions were enhanced due to plasticization of fructose on maltodextrin, which is explained through free volume theory.

Performance analysis using equivariant kernel density estimator in nonlinear mixture

This paper investigates the performance analysis of separation of mutually independent sources in nonlinear models. The nonlinear mapping constituted by an unsupervised linear mixture is followed by an unknown and invertible nonlinear distortion, are found in many signal processing cases. Generally, blind separation of sources from their nonlinear mixtures is rather difficult. We propose using a kernel density estimator incorporated with equivariant gradient analysis to separate the sources with nonlinear distortion. The kernel density estimator parameters of which are iteratively updated to minimize the output independence expressed as a mutual information criterion. The equivariant gradient algorithm has the form of nonlinear decorrelation to perform the convergence analysis. Experiments are proposed to illustrate these results.