Paramaterless Clustering Techniques for Gene Expression Analysis

Paramaterless Clustering Techniques for Gene Expression Analysis

Vincent S. Tseng (National Cheng Kung University, Taiwan) and Ching-Pin Kao (National Cheng Kung University, Taiwan)
Copyright: © 2006 |Pages: 19
DOI: 10.4018/978-1-59140-863-5.ch009
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Abstract

In recent years, clustering analysis has even become a valuable and useful tool for in-silico analysis of microarray or gene expression data. Although a number of clustering methods have been proposed, they are confronted with difficulties in meeting the requirements of automation, high quality, and high efficiency at the same time. In this chapter, we discuss the issue of parameterless clustering technique for gene expression analysis. We introduce two novel, parameterless and efficient clustering methods that fit for analysis of gene expression data. The unique feature of our methods is they incorporate the validation techniques into the clustering process so that high quality results can be obtained. Through experimental evaluation, these methods are shown to outperform other clustering methods greatly in terms of clustering quality, efficiency, and automation on both of synthetic and real data sets.

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Table of Contents
Acknowledgments
Hui-Huang Hsu
Chapter 1
Hui-Huang Hsu
Bioinformatics uses information technologies to facilitate the discovery of new knowledge in molecular biology. Among the information technologies... Sample PDF
Introduction to Data Mining in Bioinformatics
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Chapter 2
Li Liao
Recently, clustering and classification methods have seen many applications in bioinformatics. Some are simply straightforward applications of... Sample PDF
Hierarchical Profiling, Scoring and Applications in Bioinformatics
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Chapter 3
D. Frank Hsu, Yun-Sheng Chung, Bruce S. Kristal
Combination methods have been investigated as a possible means to improve performance in multi-variable (multi-criterion or multi-objective)... Sample PDF
Combinatorial Fusion Analysis: Methods and Practices of Combining Multiple Scoring Systems
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Chapter 4
Hsuan T. Chang
This chapter introduces various visualization (i.e., graphical representation) schemes of symbolic DNA sequences, which are basically represented by... Sample PDF
DNA Sequence Visualization
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Chapter 5
Simon Lin, Salvatore Mungal, Richard Haney, Edward F. Patz Jr., Patrick McConnell
This chapter provides a rudimentary review of the field of proteomics as it applies to mass spectrometry, data handling, and analysis. It points out... Sample PDF
Proteomics with Mass Spectrometry
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Chapter 6
Sven Rahmann, Tobias Muller, Thomas Dandekar, Matthias Wolf
The goal of phylogenetics is to reconstruct ancestral relationships between different taxa, e.g., different species in the tree of life, by means of... Sample PDF
Efficient and Robust Analysis of Large Phylogenetic Datasets
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Chapter 7
Tatsuya Akutsu
This chapter provides an overview of computational problems and techniques for protein threading. Protein threading is one of the most powerful... Sample PDF
Algorithmic Aspects of Protein Threading
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Chapter 8
Arpad Kelemen, Yulan Liang
Pattern differentiations and formulations are two main research tracks for heterogeneous genomic data pattern analysis. In this chapter, we develop... Sample PDF
Pattern Differentiations and Formulations for Heterogeneous Genomic Data through Hybrid Approaches
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Chapter 9
Vincent S. Tseng, Ching-Pin Kao
In recent years, clustering analysis has even become a valuable and useful tool for in-silico analysis of microarray or gene expression data.... Sample PDF
Paramaterless Clustering Techniques for Gene Expression Analysis
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Chapter 10
Junying Zhang
This chapter introduces gene selection approaches in microarray data analysis for two purposes: cancer classification and tissue heterogeneity... Sample PDF
Joint Discriminatory Gene Selection for Molecular Classification of Cancer
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Chapter 11
Takashi Kido
This chapter introduces computational methods for detecting complex disease loci with haplotype analysis. It argues that the haplotype analysis... Sample PDF
A Haplotype Analysis System for Genes Discovery of Common Diseases
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Chapter 12
Peng-Yeng yin, Shyong-Jian Shyu, Guan-Shieng Huang, Shuang-Te Liao
With the advent of new sequencing technology for biological data, the number of sequenced proteins stored in public databases has become an... Sample PDF
A Bayesian Framework for Improving Clustering Accuracy of Protein Sequences Based on Association Rules
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Chapter 13
Byung-Hoon Park, Phuongan Dam, Chongle Pan, Ying Xu, Al Geist, Grant Heffelfinger, Nagiza F. Samatova
Protein-protein interactions are fundamental to cellular processes. They are responsible for phenomena like DNA replication, gene transcription... Sample PDF
In Silico Recognition of Protein-Protein Interaction: Theory and Applications
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Chapter 14
Christopher Besemann, Anne Denton, Ajay Yekkirala, Ron Hutchison, Marc Anderson
In this chapter, we discuss the use of differential association rules to study the annotations of proteins in one or more interaction networks.... Sample PDF
Differential Association Rules: Understanding Annotations in Protein Interaction Networks
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Chapter 15
Francisco M. Couto, Mario J. Silva
This chapter introduces the use of Text Mining in scientific literature for biological research, with a special focus on automatic gene and protein... Sample PDF
Mining BioLiterature: Toward Automatic Annotation of Genes and Proteins
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Chapter 16
Kwangmin Choi, Sun Kim
Understanding the genetic content of a genome is a very important but challenging task. One of the most effective methods to annotate a genome is to... Sample PDF
Comparative Genome Annotation Systems
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