Detecting genetic variation in microarray expression data

Detecting Changes in Alternative mRNA Processing From Microarray Expression Data

Detecting Changes in Alternative mRNA Processing From Microarray Expression Data

cDNA Microarray Data Normalization

Detecting outlier samples in microarray data.

In this paper, we address the problem of detecting outlier samples with highly different expression patterns in microarray data. Although outliers are not common, they appear even in widely used benchmark data sets and can negatively affect microarray data analysis. It is important to identify outliers in order to explore underlying experimental or biological problems and remove erroneous data....

A Genetic Approach for Gene Selection on Microarray Expression Data

Microarrays allow simultaneous measurement of the expression levels of thousands of genes in cells under different physiological or disease states. Because the number of genes exceeds the number of samples, class prediction on microarray expression data leads to an extreme “curse of dimensionality” problem. A principal goal of these studies is to identify a subset of informative genes for class...

Consensus clustering of gene expression microarray data using genetic algorithms

This work presents a new consensus clustering method for gene expression microarray data based on a genetic algorithm. Using two datasets – DA and DB – as input, the genetic algorithm examines putative partitions for the samples in DA, selecting biomarkers that support such partitions. The biomarkers are then used to build a classifier which is used in DB to determine its samples classes. The g...