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Handbook of Research on Computational Intelligence Applications in Bioinformatics

Book Description

Developments in the areas of biology and bioinformatics are continuously evolving and creating a plethora of data that needs to be analyzed and decrypted. Since it can be difficult to decipher the multitudes of data within these areas, new computational techniques and tools are being employed to assist researchers in their findings. The Handbook of Research on Computational Intelligence Applications in Bioinformatics examines emergent research in handling real-world problems through the application of various computation technologies and techniques. Featuring theoretical concepts and best practices in the areas of computational intelligence, artificial intelligence, big data, and bio-inspired computing, this publication is a critical reference source for graduate students, professionals, academics, and researchers.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  6. Preface
    1. COMPUTATIONAL INTELLIGENCE TECHNIQUES
    2. ORGANIZATION OF THE BOOK
    3. REFERENCES
  7. Acknowledgment
  8. Section 1: Big Data Mining and Pattern Discovery
    1. Chapter 1: Unleashing Artificial Intelligence onto Big Data A Review
      1. ABSTRACT
      2. INTRODUCTION
      3. BIG DATA: AN OVERVIEW
      4. CONCLUSION
      5. REFERENCES
    2. Chapter 2: Hybrid Ensemble Learning Methods for Classification of Microarray Data
      1. ABSTRACT
      2. INTRODUCTION
      3. RELEVANCE AND REDUNDANCY OF FEATURES
      4. ENSEMBLE FRAMEWORK AND FEATURE SLECTION METHOD
      5. EXPERIMENTAL STUDY
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    3. Chapter 3: Recent Trends in Spatial Data Mining and Its Challenges
      1. ABSTRACT
      2. INTRODUCTION
      3. CHARACTERISTIC OF SPATIAL DATA
      4. MAIN METHODS OF SPATIAL DATA MINING
      5. SPATIAL CLUSTERING METHODS
      6. SPATIAL CO-LOCATION MINING
      7. SPATIAL ASSOCIATION RULE
      8. UNCERTAINITY IN SPATIAL DATA MINING
      9. SPATIAL TREND ANALYSIS
      10. SPATIAL DATA MINING PROBLEMS
      11. APPLICATION OF SPATIAL DATA MINING
      12. CONCLUSION
      13. REFERENCES
      14. KEY TERMS AND DEFINITIONS
    4. Chapter 4: Knowledge Representation A Semantic Network Approach
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. SEMANTIC NETWORK BASED SEARCH (AN EXAMPLE)
      6. ALGORITHM USED
      7. RESULTS AND DISCUSSION
      8. FUZZY SEMANTIC NETWORK
      9. SOLUTIONS AND RECOMMENDATIONS
      10. FUTURE RESEARCH DIRECTIONS
      11. CONCLUSION
      12. REFERENCES
  9. Section 2: Computational Intelligence in Bioinformatics
    1. Chapter 5: Development of Novel Multi-Objective Based Model for Protein Structural Class Prediction
      1. ABSTRACT
      2. INTRODUCTION
      3. SIGNIFICANCE OF PROTEIN SECONDARY STRUCTURE
      4. CLASSIFICATION USING RADIAL BASIS FUNCTION NEURAL NETWORK (RBF)
      5. WHAT IS OPTIMIZATION?
      6. WHY EVOLUTIONARY ALGORITHM (EA)?
      7. DOMINANCE AND NON-DOMINANCE
      8. NON-ELITIST MULTI-OBJECTIVE GENETIC ALGORITHM (NSGA)
      9. NON-DOMINATED SORTING GENETIC ALGORITHM (NSGA-II)
      10. DIFFERENT GENETIC OPERATORS OF NSGA-II ALGORITHM
      11. RE-SUBSTITUTION TEST
      12. SIMULATION RESULTS OF CLASSIFICATION USING FLANN
      13. SIMULATION RESULTS OF CLASSIFICATION USING RBFNN AND NSGA-II
      14. CONCLUSION
      15. REFERENCES
      16. KEY TERMS AND DEFINITIONS
    2. Chapter 6: Rough Fuzzy Set Theory and Neighbourhood Approximation Based Modelling for Spatial Epidemiology
      1. ABSTRACT
      2. BACKGROUND
      3. EPIDEMIOLOGY
      4. SPATIAL RELATIONSHIPS
      5. DECISION SYSTEMS
      6. FUZZY FLAIR TO DECISION SYSTEMS
      7. CONCLUSION AND FUTURE DIRECTIONS
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    3. Chapter 7: Applying CI in Biology through PSO
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. CI METHODS FOR SOLVING COMPUTATIONAL BIOLOGY PROBLEMS
      6. SOLUTIONS AND RECOMMENDATIONS
      7. FUTURE RESEARCH DIRECTION
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
      11. APPENDIX
    4. Chapter 8: Application of Rough Set Based Models in Medical Diagnosis
      1. ABSTRACT
      2. INTRODUCTION
      3. DEFINITIONS AND NOTATIONS
      4. LITERATURE REVIEW
      5. APPLICATIONS
      6. CONCLUSION
      7. FUTURE WORKS
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    5. Chapter 9: Application of Uncertainty Models in Bioinformatics
      1. ABSTRACT
      2. INTRODUCTION
      3. FUZZY SET
      4. APPLICATION OF FUZZY SETS IN MEDICAL DIAGNOSIS
      5. SOFT SET IN BIOINFORMATICS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
  10. Section 3: Nature-Inspired Computing for Analysis of DNA and Protein Microarray Data
    1. Chapter 10: Computational Methods for Prediction of Protein-Protein Interactions
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. NETWORK TOPOLOGY BASED METHODS
      6. DATA MINING BASED METHOD FOR PPI PREDICTION
      7. MACHINE LEARNING BASED METHODS FOR PPI PREDICTION
      8. TOOLS AND DATABASES FOR PPI PREDICTION
      9. SOLUTIONS AND RECOMMENDATIONS
      10. FUTURE RESEARCH DIRECTIONS
      11. CONCLUSION
      12. CASE STUDY
      13. REFERENCES
      14. KEY TERMS AND DEFINITIONS
    2. Chapter 11: Analysis of Microarray Data using Artificial Intelligence Based Techniques
      1. ABSTRACT
      2. INTRODUCTION
      3. APPLICATIONS OF MICROARRAYS
      4. ARTIFICIAL INTELLIGENCE TECHNIQUES AND MICROARRAY ANALYSIS
      5. CONCLUSION AND FUTURE CHALLENGES
      6. FUTURE CHALLENGES
      7. ACKNOWLEDGMENT
      8. REFERENCES
    3. Chapter 12: Extraction of Protein Sequence Motif Information using Bio-Inspired Computing
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. METHODS AND MATERIAL
      5. PROPOSED METHOD: MOTIF EXTRACTION USING PSO K-MEANS
      6. RESULTS AND DISCUSSIONS
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
      10. ENDNOTE
    4. Chapter 13: Study of Basic Concepts on the Development of Protein Microarray - Gene Expression Profiling
      1. ABSTRACT
      2. INTRODUCTION
      3. NEED FOR PROTEIN MICROARRAY
      4. FABRICATION OF PROTEIN MICROARRAY
      5. PRINTING TECHNOLOGY
      6. DIFFERENT TYPES OF PROTEIN MICROARRAY
      7. CELL BASED MICROARRAY TECHNOLOGIES
      8. CELL-FREE EXPRESSION BASED PROTEIN MICROARRAYS
      9. DETECTION SYSTEM FOR PROTEIN MICROARRAYS
      10. LABEL-FREE DETECTION TECHNOLOGY
      11. PROTEIN MICROARRAY DATA ANALYSIS
      12. VALIDATION
      13. APPLICATION OF PROTEIN MICROARRAY
      14. ISSUE AND RESOLUTION PROTEIN MICROARRAY FABRICATION AND DETECTION
      15. CONCLUSION
      16. REFERENCES
      17. KEY TERMS AND DEFINITIONS
    5. Chapter 14: Personalized Medicine in the Era of Genomics
      1. ABSTRACT
      2. INTRODUCTION
      3. PHARMACOGENOMICS AND PHARMACOPROTEOMICS: KEY COMPONENTS OF PERSONALIZED THERAPY
      4. METABOLOMIC ANALYSIS FOR PERSONALIZED MEDICINE
      5. BIOMARKERS
      6. NANOBIOTECHNOLOGY IN PERSONALIZED MEDICINE
      7. BIOINFORMATICS TOOLS AND DATABASES AIDING PERSONALIZED MEDICINE
      8. IMMNUNOINFORMATICS AND PERSONALIZED MEDICINE
      9. DISEASE-BASED CASE STUDIES ON PERSONALIZED MEDICINE
      10. ELSI (ETHICAL, LEGAL, AND SOCIAL IMPLICATIONS) OF PERSONALIZED MEDICINE
      11. CHALLENGES IN THE JOURNEY TOWARDS PERSONALIZED MEDICINE
      12. CURRENT STATUS AND FUTURE IMPLICATIONS OF PERSONALIZED MEDICINE
      13. CONCLUSION
      14. REFERENCES
      15. KEY TERMS AND DEFINITIONS
  11. Section 4: Bio-Inspired Algorithms and Engineering Applications
    1. Chapter 15: Evolutionary Computing Approaches to System Identification
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. EVOLUTIONARY ALGORITHMS PARADIGMS
      4. 3. RECENT EVOLUTIONARY COMPUTING APPROACHES
      5. 4. PARAMETER IDENTIFICATION OF INDUCTION MOTOR USING DE AND OMDE
      6. 5. NONLINEAR SYSTEM IDENTIFICATION USING HYBRID DIFFERENTIAL EVOLUTION TRAINING ALGORITHM
      7. 6. CHAPTER SUMMARY
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    2. Chapter 16: BFO Optimized Automatic Load Frequency Control of a Multi-Area Power System
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. DYNAMICS OF THE POWER SYSTEM
      5. DESIGN MODEL FOR VARIOUS SYSTEMS
      6. SIMULATION RESULTS OF AUTOMATIC LOAD FREQUENCY CONTROL
      7. OBSERVATION
      8. BACTERIA FORAGING OPTIMIZATION ALGORITHM (BFOA)
      9. SIMULATION RESULTS AND DISCUSSIONS
      10. CONCLUSION
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    3. Chapter 17: Solution of Some Differential Equation in Fuzzy Environment by Extension Principle Method and Its Application in Biomathematics
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. PRELIMINARIES
      4. 3. FUZZY DIFFERENTIAL EQUATION: LINEAR AND NON LINEAR
      5. 4. TECHNIQUES FOR SOLVING FUZZY DIFFERENTIAL EQUATION
      6. 5. BIO-MATHEMATICAL PROBLEM IN FUZZY ENVIRONMENT AND ITS SOLUTION
      7. 6. FUTURE SCOPE
      8. 7. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    4. Chapter 18: Application of Computational Intelligence Techniques in Wireless Sensor Networks the State of the Art
      1. ABSTRACT
      2. INTRODUCTION
      3. APPLICATIONS OF WSN
      4. CONSTRAINTS AND CHALLENGES
      5. COVERAGE PROBLEM
      6. AREA COVERAGE IN LITERATURE
      7. COMPUTATIONAL INTELLIGENCE (CI) AND CLUSTERING IN WIRELESS SENSOR NETWORK
      8. OPEN CHALLENGES
      9. CONCLUSION
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
  12. Compilation of References
  13. About the Contributors