An Integrated Framework for Fuzzy Classification and Analysis of Gene Expression Data

An Integrated Framework for Fuzzy Classification and Analysis of Gene Expression Data

Mohammad Khabbaz (University of Calgary, Canada), Keivan Kianmehr (University of Calgary, Canada), Mohammad Alshalalfa (University of Calgary, Canada) and Reda Alhajj (University of Calgary, Canada)
DOI: 10.4018/978-1-60566-717-1.ch009
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Abstract

This chapter takes advantage of using fuzzy classifier rules to capture the correlations between genes. The main motivation to conduct this study is that a fuzzy classifier rule is essentially an “if-then” rule that contains linguistic terms to represent the feature values. This representation of a rule that demonstrates the correlations among the genes is very simple to understand and interpret for domain experts. In this proposed gene selection procedure, instead of measuring the effectiveness of every single gene for building the classifier model, the authors incorporate the impotence of a gene correlation with other existing genes in the process of gene selection. That is, a gene is rejected if it is not in a significant correlation with other genes in the dataset. Furthermore, in order to improve the reliability of this approach, the process is repeated several times in these experiments, and the genes reported as the result are the genes selected in most experiments. This chapter reports test results on ?ve datasets and analyzes the achieved results from biological perspective.
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Introduction

Gene expression analysis is the use of quantitative RNA measurements of gene expression in order to characterize biological processes and clarify the mechanisms of gene transcription. The microarray technology makes it possible to monitor the expression levels of tens of thousands of genes in parallel. It measures the relative RNA levels of genes and produces high dimensional dataset, which is in turn a highly informative source for gene expression analysis. The microarray data from a series of N experiments may be represented as N×M gene expression matrix in which each of the N rows consists of an M-element expression vector. The latter vector represents the gene expression level of the genes for a single experiment.

Table 1 displays an example matrix for gene expression data, where the rows denote different samples or conditions (such as the same cell type among different samples), while the columns denote genes; F [Sample 3, G4] denotes the quantitative expression of gene G4 in Sample 3.

Table 1.
Example gene expression data
SampleG1G2G3G4
Sample152395916
Sample257326421
Sample330766165
Sample452395916
Sample518795664
Sample665815970
Sample741832123

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