Developing Enhanced Models for Analysis of Gene Expression Data

Abstract

Abstract DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Shedding the light on the hidden patterns in gene expression data offers an immense chance for a better understanding of functional genomics. However, the huge number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A crucial step toward addressing this challenge is the find sets of co-regulated genes or sets of active genes under only subsets of experimental conditions, which is essential in the data mining process to reveal natural structures and identify interesting local patterns in the underlying data.