Gene Expression Data
| TABLE OF CONTENT | | CHAPTERNO | TITLE | PAGE NO | | ABSTRACT | vi | | LIST OF FIGURE | viii | | LIST OF ABBREVATIONS | ix | 1 | INTRODUCTION | 1 | | 1.1 Background and Motivation | 1 | | 1.2 Introduction to Microarray Technology | 7 | | 1.2.1 Measuring mRNA levels | 7 | | 1.2.2 Pre-processing of Gene Expression Data | 8 | | 1.2.3 Applications of Clustering Gene Expression Data | 9 | | 1.3 Mutual Information | 10 | | 1.4 Introduction to Clustering Techniques | 11 | | 1.4.1 Clusters and Clustering | 11 | | 1.4.2 Categories of Gene Expression Data Clustering | 11 | | 1.5 Semi-supervised Learning | 12 | | 1.5.1 Semi-supervised Classification | 12 | | 1.5.2 Semi-supervised Clustering | 13 | | 1.6 Motivation of the Project | 14 | | | | | | | | | | | | | | | | | | | | | | 2 | LITERATURE SURVEY | 15 | | 2.1 Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data | 15 | | 2.2 Analysis of microarray gene expression data | 16 | | 2.3 Supervised Clustering of Genes | 17 | | 2.4 ‘Gene shaving’ as a method for identifying distinct sets of genes with similar expression patterns | 18 | | 2.5 Minimum Redundancy Feature Selection from Microarray Gene Expression Data | 19 | | 2.6 Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy | 20 | | 2.7 Using Mutual Information for Selecting Features in Supervised Neural Net Learning | 21 | | 2.8 Minimum Redundancy Feature Selection From Microarray Gene expression Data | 21 | | 2.9 Microarray Gene Expression Analysis Using Type 2 Fuzzy Logic | 22 | | 2.10 Class Discovery in Gene Expression Data | 23 | 3 | SYSTEM ANALYSIS AND DESIGN | 24 | | 3.1 Existing system | 24 | | 3.1.1
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