Contents

Cover

Half Title page

Title page

Copyright page

Dedication

Preface

Contributors

Part I: Introduction

Chapter 1: Computational Intelligence: Foundations, Perspectives, and Recent Trends

1.1 What is Computational Intelligence?

1.2 Classical Components of CI

1.3 Hybrid Intelligent Systems in CI

1.4 Emerging Trends in CI

1.5 Summary

References

Chapter 2: Fundamentals of Pattern Analysis: A Brief Overview

2.1 Introduction

2.2 Pattern Analysis: Basic Concepts and Approaches

2.3 Feature Selection

2.4 Pattern Classification

2.5 Unsupervised Classification or Clustering

2.6 Neural Network Classifier

2.7 Conclusion

References

Chapter 3: Biological Informatics: Data, Tools, and Applications

3.1 Introduction

3.2 Data

3.3 Tools

3.4 Applications

3.5 Conclusion

References

Part II: Sequence Analysis

Chapter 4: Promoter Recognition Using Neural Network Approaches

4.1 Introduction

4.2 Related Literature /Background

4.3 Global Signal-Based Methods for Promoter Recognition

4.4 Challenges in Promoter Classification

4.5 Conclusions

4.6 Future directions

References

Chapter 5: Predicting Microrna Prostate Cancer Target Genes

5.1 Introduction

5.2 miRNA and Prostate Cancer

5.3 Prediction software for miRNAs

5.4 miRanda

5.5 Proposed method

5.6 Automatic parameter tuning

5.7 Experimental analysis

5.8 Discussion and Conclusions

Acknowledgments

References

Part III: Structure Analysis

Chapter 6: Structural Search in RNA Motif Databases

6.1 Introduction

6.2 The Search Engine on RmotifDB

6.3 The Search Engine Based on BlockMatch

6.4 Conclusion

Acknowledgments

References

Chapter 7: Kernels on Protein Structures

7.1 Introduction

7.2 Kernels Methods

7.3 Protein Structures

7.4 Kernels on Neighborhoods

7.5 Kernels on Protein Structures

7.6 Experimental Results

7.7 Discussion and Conclusion

Appendix A

References

Chapter 8: Characterization of Conformational Patterns in Active and Inactive Forms of Kinases Using Protein Blocks Approach

8.1 Introduction

8.2 Distinguishing conformational variations from rigid-body shifts in active and inactive forms of a kinase

8.3 Cross comparison of active and inactive forms of closely related kinases

8.4 Comparison of the active states of homologous kinases

8.5 Conclusions

Acknowledgments

References

Chapter 9: Kernel Function Applications in Cheminformatics

9.1 Introduction

9.2 Background

9.3 Related Works

9.4 Alignment Kernels with Pattern-based Features

9.5 Alignment Kernels with Approximate Pattern Features

9.6 Matching Kernels with Approximate Pattern-based Features

9.7 Graph Wavelets for Topology Comparison

9.8 Conclusions

References

Chapter 10: In Silico Drug Design Using a Computational Intelligence Technique

10.1 Introduction

10.2 Proposed Methodology

10.3 Experimental Results and Discussion

10.4 Conclusion

References

Part IV: Microarray Data Analysis

Chapter 11: Integrated Differential Fuzzy Clustering for Analysis of Microarray Data

11.1 Introduction

11.2 Clustering Algorithms and Validity Measure

11.3 Differential Evolution based Fuzzy Clustering

11.4 Experimental Results

11.5 Integrated Fuzzy clustering with Support Vector Machines

11.6 Conclusion

References

Chapter 12: Identifying Potential Gene Markers Using Svm Classifier Ensemble

12.1 Introduction

12.2 Microarray Gene Expression Data

12.3 Support Vector Machine Classifier

12.4 Proposed Technique

12.5 Data Sets and Preprocessing

12.6 Experimental Results

12.7 Discussion and Conclusions

Acknowledgment

References

Chapter 13: Gene Microarray Data Analysis Using Parallel Point Symmetry-Based Clustering

13.1 Introduction

13.2 Symmetry- and point symmetry-based distance measures

13.3 Parpsbkm clustering implementation

13.4 Performance analysis

13.5 Test for Statistical Significance

13.6 Conclusions

References

Part V: Systems Biology

Chapter 14: Techniques For Prioritization of Candidate Disease Genes

14.1 Introduction

14.2 Prioritization Based on Text-Mining With Reference to Phenotypes

14.3 Prioritization with no direct reference to phenotypes

14.4 Prioritization using interaction networks

14.5 Prioritization based on joint use of interaction network and literature-based similarity between phenotypes

14.6 Fusion of data from multiple sources

14.7 Conclusions and open problems

14.8 Acknowledgment

References

Chapter 15: Prediction of Protein–Protein Interactions

15.1 Introduction

15.2 Basic Definitions

15.3 Classification of PPI

15.4 Characteristics of PPIs

15.5 Driving Forces for the Formation of PPIs

15.6 Prediction of PPIs

15.7 Discussion and Conclusion

Appendix I

Appendix II

References

Chapter 16: Analyzing Topological Properties of Protein–Protein Interaction Networks: A Perspective Toward Systems Biology

16.1 Introduction

16.2 Topology of PPI Networks

16.3 Literature Survey

16.4 Problem Discussion

16.5 Theoretical Analysis

16.6 Algorithmic Approach

16.7 Empirical Analysis

16.8 Conclusions

Acknowledgment

References

Index