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Computational Intelligence and Pattern Analysis in Biological Informatics

Book Description

An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner.

This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers

  • Chapters authored by leading researchers in CI in biology informatics.

  • Covers highly relevant topics: rational drug design; analysis of microRNAs and their involvement in human diseases.

  • Supplementary material included: program code and relevant data sets correspond to chapters.

Note: The ebook version does not provide access to the companion files.

Table of Contents

  1. Cover
  2. Half Title page
  3. Title Page
  4. Copyright page
  5. Dedication
  6. Preface
  7. Contributors
  8. Part I: Introduction
    1. Chapter 1: Computational Intelligence: Foundations, Perspectives, and Recent Trends
      1. 1.1 What is Computational Intelligence?
      2. 1.2 Classical Components of CI
      3. 1.3 Hybrid Intelligent Systems in CI
      4. 1.4 Emerging Trends in CI
      5. 1.5 Summary
      6. References
    2. Chapter 2: Fundamentals of Pattern Analysis: A Brief Overview
      1. 2.1 Introduction
      2. 2.2 Pattern Analysis: Basic Concepts and Approaches
      3. 2.3 Feature Selection
      4. 2.4 Pattern Classification
      5. 2.5 Unsupervised Classification or Clustering
      6. 2.6 Neural Network Classifier
      7. 2.7 Conclusion
      8. References
    3. Chapter 3: Biological Informatics: Data, Tools, and Applications
      1. 3.1 Introduction
      2. 3.2 Data
      3. 3.3 Tools
      4. 3.4 Applications
      5. 3.5 Conclusion
      6. References
  9. Part II: Sequence Analysis
    1. Chapter 4: Promoter Recognition Using Neural Network Approaches
      1. 4.1 Introduction
      2. 4.2 Related Literature /Background
      3. 4.3 Global Signal-Based Methods for Promoter Recognition
      4. 4.4 Challenges in Promoter Classification
      5. 4.5 Conclusions
      6. 4.6 Future directions
      7. References
    2. Chapter 5: Predicting Microrna Prostate Cancer Target Genes
      1. 5.1 Introduction
      2. 5.2 miRNA and Prostate Cancer
      3. 5.3 Prediction software for miRNAs
      4. 5.4 miRanda
      5. 5.5 Proposed method
      6. 5.6 Automatic parameter tuning
      7. 5.7 Experimental analysis
      8. 5.8 Discussion and Conclusions
      9. Acknowledgments
      10. References
  10. Part III: Structure Analysis
    1. Chapter 6: Structural Search in RNA Motif Databases
      1. 6.1 Introduction
      2. 6.2 The Search Engine on RmotifDB
      3. 6.3 The Search Engine Based on BlockMatch
      4. 6.4 Conclusion
      5. Acknowledgments
      6. References
    2. Chapter 7: Kernels on Protein Structures
      1. 7.1 Introduction
      2. 7.2 Kernels Methods
      3. 7.3 Protein Structures
      4. 7.4 Kernels on Neighborhoods
      5. 7.5 Kernels on Protein Structures
      6. 7.6 Experimental Results
      7. 7.7 Discussion and Conclusion
      8. Appendix A
      9. References
    3. Chapter 8: Characterization of Conformational Patterns in Active and Inactive Forms of Kinases Using Protein Blocks Approach
      1. 8.1 Introduction
      2. 8.2 Distinguishing conformational variations from rigid-body shifts in active and inactive forms of a kinase
      3. 8.3 Cross comparison of active and inactive forms of closely related kinases
      4. 8.4 Comparison of the active states of homologous kinases
      5. 8.5 Conclusions
      6. Acknowledgments
      7. References
    4. Chapter 9: Kernel Function Applications in Cheminformatics
      1. 9.1 Introduction
      2. 9.2 Background
      3. 9.3 Related Works
      4. 9.4 Alignment Kernels with Pattern-based Features
      5. 9.5 Alignment Kernels with Approximate Pattern Features
      6. 9.6 Matching Kernels with Approximate Pattern-based Features
      7. 9.7 Graph Wavelets for Topology Comparison
      8. 9.8 Conclusions
      9. References
    5. Chapter 10: In Silico Drug Design Using a Computational Intelligence Technique
      1. 10.1 Introduction
      2. 10.2 Proposed Methodology
      3. 10.3 Experimental Results and Discussion
      4. 10.4 Conclusion
      5. References
  11. Part IV: Microarray Data Analysis
    1. Chapter 11: Integrated Differential Fuzzy Clustering for Analysis of Microarray Data
      1. 11.1 Introduction
      2. 11.2 Clustering Algorithms and Validity Measure
      3. 11.3 Differential Evolution based Fuzzy Clustering
      4. 11.4 Experimental Results
      5. 11.5 Integrated Fuzzy clustering with Support Vector Machines
      6. 11.6 Conclusion
      7. References
    2. Chapter 12: Identifying Potential Gene Markers Using Svm Classifier Ensemble
      1. 12.1 Introduction
      2. 12.2 Microarray Gene Expression Data
      3. 12.3 Support Vector Machine Classifier
      4. 12.4 Proposed Technique
      5. 12.5 Data Sets and Preprocessing
      6. 12.6 Experimental Results
      7. 12.7 Discussion and Conclusions
      8. Acknowledgment
      9. References
    3. Chapter 13: Gene Microarray Data Analysis Using Parallel Point Symmetry-Based Clustering
      1. 13.1 Introduction
      2. 13.2 Symmetry- and point symmetry-based distance measures
      3. 13.3 Parpsbkm clustering implementation
      4. 13.4 Performance analysis
      5. 13.5 Test for Statistical Significance
      6. 13.6 Conclusions
      7. References
  12. Part V: Systems Biology
    1. Chapter 14: Techniques For Prioritization of Candidate Disease Genes
      1. 14.1 Introduction
      2. 14.2 Prioritization Based on Text-Mining with Reference to Phenotypes
      3. 14.3 Prioritization with no direct reference to phenotypes
      4. 14.4 Prioritization using interaction networks
      5. 14.5 Prioritization based on joint use of interaction network and literature-based similarity between phenotypes
      6. 14.6 Fusion of data from multiple sources
      7. 14.7 Conclusions and open problems
      8. 14.8 Acknowledgment
      9. References
    2. Chapter 15: Prediction of Protein–Protein Interactions
      1. 15.1 Introduction
      2. 15.2 Basic Definitions
      3. 15.3 Classification of PPI
      4. 15.4 Characteristics of PPIs
      5. 15.5 Driving Forces for the Formation of PPIs
      6. 15.6 Prediction of PPIs
      7. 15.7 Discussion and Conclusion
      8. Appendix I
      9. Appendix II
      10. References
    3. Chapter 16: Analyzing Topological Properties of Protein–Protein Interaction Networks: A Perspective Toward Systems Biology
      1. 16.1 Introduction
      2. 16.2 Topology of PPI Networks
      3. 16.3 Literature Survey
      4. 16.4 Problem Discussion
      5. 16.5 Theoretical Analysis
      6. 16.6 Algorithmic Approach
      7. 16.7 Empirical Analysis
      8. 16.8 Conclusions
      9. Acknowledgment
      10. References
  13. Index