Application of Microarray Data Set Analysis for Dna Gene Expression Data

Abstract

DNA microarray technology has newly made it possible to simultaneously monitor the
expressions of thousands of genes during important biological processes and across
collections of related Sample Data. Enlighten the patterns hidden in gene expression data
offers a tremendous opportunity for an enhanced understanding of functional genomics.
However, the various 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. At the begin step toward addressing this
challenge is the use of clustering techniques, which is essential in the data mining process to
reveal natural structures and identify interesting patterns in the underlying data. Cluster
analysis seeks to partition a given data set into groups based on specified features so that the
data points within a group are more similar to each other than the points in different groups.
Many clustering algorithms have been adapted or directly applied to gene expression data,
and also new algorithms have recently been proposed specifically aiming at gene expression
data. These clustering algorithms have been proven useful for identifying biologically
relevant groups of gene expressions from sample data. In this paper, we start introduce the
concepts of microarray technology and discuss the base elements of clustering on gene
expression data. In
particular, we split the cluster analysis for gene expression data into three categories. Then
we present specific challenges pertinent to each clustering category and introduce several
representative approaches. We also deals the problem of cluster validation in three aspects
and review various methods to assess the quality and reliability of clustering results.