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Part III: Structure Analysis
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Part III: Structure Analysis
by Jason T. Wang, Sanghamitra Bandyopadhyay, Ujjwal Maulik
Computational Intelligence and Pattern Analysis in Biological Informatics
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
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Chapter 5: Predicting Microrna Prostate Cancer Target Genes
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Chapter 6: Structural Search in RNA Motif Databases
PART III
STRUCTURE ANALYSIS
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