Topological Analysis and Sub-Network Mining of Protein-Protein Interactions

Topological Analysis and Sub-Network Mining of Protein-Protein Interactions

Daniel Wu (Drexel University, USA) and Xiaohua Hu (Drexel University, USA)
Copyright: © 2007 |Pages: 32
DOI: 10.4018/978-1-59904-271-8.ch008
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Abstract

In this chapter, we report a comprehensive evaluation of the topological structure of protein-protein interaction (PPI) networks, by mining and analyzing graphs constructed from the popular data sets publicly available to the bioinformatics research community. We compare the topology of these networks across different species, different confidence levels, and different experimental systems used to obtain the interaction data. Our results confirm the well-accepted claim that the degree distribution follows a power law. However, further statistical analysis shows that residues are not independent on the fit values, indicating that the power law model may be inadequate. Our results also show that the dependence of the average clustering coefficient on the vertices degree is far from a power law, contradicting many published results. For the first time, we report that the average vertex density exhibits a strong powder law dependence on the vertices degree for the networks studied, regardless of species, confidence levels, and experimental systems. We also present an efficient and accurate approach to detecting a community in a protein-protein interaction network from a given seed protein. Our experimental results show strong structural and functional relationships among member proteins within each of the communities identified by our approach, as verified by MIPS complex catalog database and annotations.

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Table of Contents
Preface
David Taniar
Chapter 1
Torben Pedersen, Jesper Thorhauge, Søren Jespersen
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Chapter 2
Lixin Fu
In high-dimensional data sets, both the number of dimensions and the cardinalities of the dimensions are large and data is often very sparse, that... Sample PDF
Computing Dense Cubes Embedded in Sparse Data
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Chapter 3
Karlton Sequeira, Mohammed J. Zaki
Very often, related data may be collected by a number of sources, which may be unable to share their entire datasets for reasons like... Sample PDF
Exploring Similarities Across High-Dimensional Datasets
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Chapter 4
Irene Ntoutsi, Nikos Pelekis, Yannis Theodoridis
Many patterns are available nowadays due to the widespread use of knowledge discovery in databases (KDD), as a result of the overwhelming amount of... Sample PDF
Pattern Comparison in Data Mining: A Survey
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Chapter 5
Fedja Hadzic, Tharam Dillon, Henry Tan, Ling. Feng, Elizabeth Chang
Association rule mining is one of the most popular pattern discovery methods used in data mining. Frequent pattern extraction is an essential step... Sample PDF
Mining Frequent Patterns Using Self-Organizing Map
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Chapter 6
Mafruz Ashrafi, David Taniar, Kate Smith
Association rule mining is one of the most widely used data mining techniques. To achieve a better performance, many efficient algorithms have been... Sample PDF
An Efficient Compression Technique for Vertical Mining Methods
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Chapter 7
Alex Freitas, André Carvalho
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A Tutorial on Hierarchical Classification with Applications in Bioinformatics
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Chapter 8
Daniel Wu, Xiaohua Hu
In this chapter, we report a comprehensive evaluation of the topological structure of protein-protein interaction (PPI) networks, by mining and... Sample PDF
Topological Analysis and Sub-Network Mining of Protein-Protein Interactions
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Chapter 9
Yong Shi, Yi Peng, Gang Kou, Zhengxin Chen
This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria... Sample PDF
Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications
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Chapter 10
Xiuju Fu, Lipo Wang, GihGuang Hung, Liping Goh
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Linguistic Rule Extraction from Support Vector Machine Classifiers
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Chapter 11
Graph-Based Data Mining  (pages 291-307)
Wenyuan Li, Wee-Keong Ng, Kok-Leong Ong
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Graph-Based Data Mining
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Chapter 12
Richi Nayak
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Facilitating and Improving the Use of Web Services with Data Mining
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