In this work we present an improved evolutionary method for inferring S-system model of genetic networks from the time series data of gene expression. We employed Differential Ev...
: Functionally related genes involved in the same molecular-genetic, biochemical, or physiological process are often regulated coordinately Such regulation is provided by precisely...
Alexander E. Kel, Tatiana Konovalova, Tagir Valeev...
: In most studies concerning expression data analyses information on the variability of gene intensity across samples is usually exploited. This information is sensitive to initial...
Alexey V. Antonov, Igor V. Tetko, Denis Kosykh, Di...
Abstract. Clustering still represents the most commonly used technique to analyze gene expression data—be it classical clustering approaches that aim at finding biologically rel...
DNA microarray experiments generate a substantial amount of information about global gene expression. Gene expression profiles can be represented as points in multi-dimensional sp...
Lu-Yong Wang, Ammaiappan Balasubramanian, Amit Cha...
High throughput expression profiling and genotyping technologies provide the means to study the genetic determinants of population variation in gene expression variation. In this ...
In the feature selection of cancer classification problems, many existing methods consider genes individually by choosing the top genes which have the most significant signal-to...
Perhaps the most common question that a microarray study can ask is, “Between two given biological conditions, which genes exhibit changed expression levels?” Existing methods...
Will Sheffler, Eli Upfal, John Sedivy, William Sta...
When building predictors of disease state based on gene expression data, gene selection is performed in order to achieve a good performance and to identify a relevant subset of ge...
Background: DNA Microarray technology is an innovative methodology in experimental molecular biology, which has produced huge amounts of valuable data in the profile of gene expre...