Predicting proteome dynamics using gene expression data

Inferring proteome dynamics during yeast cell cycle using gene expression data

Predicting gene expression from heterogeneous data

The complexity of gene expression and the elucidation of the mechanisms involved in its regulation constitute an extremely difficult challenge in modern bioinformatics despite the amount of information made recently available by high-throughput biotechnologies and genome-wide investigations. In this contribution we investigated the effectiveness of ensemble systems for gene expression predictio...

Prediction of blood cancer using leukemia gene expression data and sparsity-based gene selection methods

Background: DNA microarray is a useful technology that simultaneously assesses the expression of thousands of genes. It can be utilized for the detection of cancer types and cancer biomarkers. This study aimed to predict blood cancer using leukemia gene expression data and a robust ℓ2,p-norm sparsity-based gene selection method. Materials and Methods: In this descriptive study, the microarray ...

Evaluating Gene Expression Dynamics Using Pairwise RNA FISH Data

Recently, a novel approach has been developed to study gene expression in single cells with high time resolution using RNA Fluorescent In Situ Hybridization (FISH). The technique allows individual mRNAs to be counted with high accuracy in wild-type cells, but requires cells to be fixed; thus, each cell provides only a "snapshot" of gene expression. Here we show how and when RNA FISH data on pai...

Predicting stochastic gene expression dynamics in single cells.

Fluctuations in protein numbers (noise) due to inherent stochastic effects in single cells can have large effects on the dynamic behavior of gene regulatory networks. Although deterministic models can predict the average network behavior, they fail to incorporate the stochasticity characteristic of gene expression, thereby limiting their relevance when single cell behaviors deviate from the pop...