Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics

Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics

Released November 2013
Publisher(s): Wiley-IEEE Press
ISBN: 9781118345788

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Book description

An in-depth look at the latest research, methods, and applications in the field of protein bioinformatics

This book presents the latest developments in protein bioinformatics, introducing for the first time cutting-edge research results alongside novel algorithmic and AI methods for the analysis of protein data. In one complete, self-contained volume, Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics addresses key challenges facing both computer scientists and biologists, arming readers with tools and techniques for analyzing and interpreting protein data and solving a variety of biological problems.

Featuring a collection of authoritative articles by leaders in the field, this work focuses on the analysis of protein sequences, structures, and interaction networks using both traditional algorithms and AI methods. It also examines, in great detail, data preparation, simulation, experiments, evaluation methods, and applications. Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics:

  • Highlights protein analysis applications such as protein-related drug activity comparison

  • Incorporates salient case studies illustrating how to apply the methods outlined in the book

  • Tackles the complex relationship between proteins from a systems biology point of view

  • Relates the topic to other emerging technologies such as data mining and visualization

  • Includes many tables and illustrations demonstrating concepts and performance figures

Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics is an essential reference for bioinformatics specialists in research and industry, and for anyone wishing to better understand the rich field of protein bioinformatics.

Table of contents

  1. Cover
  2. Series
  3. Title Page
  4. Copyright
  5. Preface
  6. Contributors
  7. Part I: From Protein Sequence to Structure
    1. Chapter 1: Emphasizing The Role of Proteins in Construction of the Developmental Genetic Toolkit in Plants
      1. 1.1 Introduction
      2. 1.2 Evolutionary Developmental (Evo-Devo) Roles in Embryogenesis of Plants (in Developmental Plant Genetic Toolkit Formation)
      3. 1.3 Phases in Embryogenesis in Arabidopsis Thaliana
      4. 1.4 Analysis
      5. 1.5 Conclusions
      6. References
      7. Bibliography
    2. Chapter 2: Protein Sequence Motif Information Discovery
      1. 2.1 Introduction
      2. 2.2 Granule Computing Approaches
      3. 2.3 Experimental Setup
      4. 2.4 Protein Sequence Motif Information Discovered by FGK Model
      5. References
    3. Chapter 3: Identifying Calcium Binding Sites in Proteins
      1. 3.1 Introduction
      2. 3.2 Methods
      3. 3.3 Results and Discussion
      4. 3.4 Conclusion
      5. References
    4. Chapter 4: Review of Imbalanced Data Learning for Protein Methylation Prediction
      1. 4.1 Introduction
      2. 4.2 Protein and Methylation
      3. 4.3 Related Works on Methylation Prediction
      4. 4.4 Conclusion
      5. Acknowledgments
      6. References
    5. Chapter 5: Analysis and Prediction of Protein Posttranslational Modification Sites
      1. 5.1 Introduction
      2. 5.2 Musite: A Machine Learning Approach
      3. 5.3 Musite Implementation
      4. 5.4 Summary
      5. Acknowledgments
      6. References
  8. Part II: Protein Analysis and Prediction
    1. Chapter 6: Protein Local Structure Prediction
      1. 6.1 Introduction
      2. 6.2 Structural Cluster Approach
      3. 6.3 Sequence Cluster Approach
      4. 6.4 Support Vector Machines for Local Protein Structure Prediction
      5. 6.5 Clustering Support Vector Machines for Local Protein Structure Prediction
      6. 6.6 Experimental Results
      7. References
    2. Chapter 7: Protein Structural Boundary Prediction
      1. 7.1 Introduction
      2. 7.2 Background
      3. 7.3 New Binary Classifiers for Protein Structural Boundary Prediction
      4. 7.4 Conclusion
      5. References
    3. Chapter 8: Prediction of RNA Binding Sites in Proteins
      1. 8.1 Introduction
      2. 8.2 Background
      3. 8.3 Framework of Prediction
      4. 8.4 Description Features of Protein RNA Binding Sites
      5. 8.5 Existing Methods
      6. 8.6 Feature Analysis and Comparison Study
      7. 8.7 Conclusion
      8. Acknowledgments
      9. References
    4. Chapter 9: Algorithmic Frameworks for Protein Disulfide Connectivity Determination
      1. 9.1 Introduction
      2. 9.2 Determining Disulfide Bonds from Sequence Information: Formulations, Features, and Algorithmic Frameworks
      3. 9.3 Algorithmic Methods for Determining Disulfide Bonds Using Mass Spectrometry
      4. 9.4 Experimental Results
      5. 9.5 Conclusions and Future Directions
      6. Acknowledgments
      7. References
    5. Chapter 10: Protein Contact Order Prediction: Update
      1. 10.1 Introduction
      2. 10.2 Correlated protein properties
      3. 10.3 Other contact measurements
      4. 10.4 Contact order calculation
      5. 10.5 Contact order prediction by homology
      6. 10.6 Contact order prediction from sequence
      7. 10.7 The public contact order web server
      8. 10.8 Conclusions
      9. References
    6. Chapter 11: Progress in Prediction of Oxidation States of Cysteines via Computational Approaches
      1. 11.1 Introduction
      2. 11.2 Survey of Previous Efforts to Predict Bonding State of Cysteine Residues on Protein Via Computational Approaches
      3. 11.3 Summary
      4. References
    7. Chapter 12: Computational Methods in CryoElectron Microscopy 3D Structure Reconstruction
      1. 12.1 Introduction
      2. 12.2 Iterative image reconstruction methods
      3. 12.3 Adaptive simultaneous algebraic reconstruction technique (ASART)
      4. 12.4 Multilevel parallel strategy for iterative reconstruction algorithm
      5. 12.5 Experimental results and discussion
      6. 12.6 Summary
      7. Acknowledgments
      8. References
  9. Part III: Protein Structure Alignment and Assessment
    1. Chapter 13: Fundamentals of Protein Structure Alignment
      1. 13.1 Introduction
      2. 13.2 Biological Motivation of Protein Structure Alignment
      3. 13.3 Mathematical Frameworks
      4. 13.4 More Recent Advances with Database Queries
      5. References
    2. Chapter 14: Discovering 3D Protein Structures for Optimal Structure Alignment
      1. 14.1 Introduction
      2. 14.2 Protein Structure
      3. 14.3 Protein Databases
      4. 14.4 Vector Space Model
      5. 14.5 Suffix Trees
      6. 14.6 Indexing 3D Protein Structures
      7. 14.7 Protein Similarity Algorithm
      8. 14.8 Summary
      9. References
    3. Chapter 15: Algorithmic Methodologies for Discovery of Nonsequential Protein Structure Similarities
      1. 15.1 Introduction
      2. 15.2 Structural Alignment
      3. 15.3 Global Sequence Order–Independent Structural Alignment
      4. 15.4 Local Sequence Order–Independent Structural Alignment
      5. 15.5 Conclusion
      6. Acknowledgments
      7. References
    4. Chapter 16: Fractal Related Methods for Predicting Protein Structure Classes and Functions
      1. 16.1 Introduction
      2. 16.2 Methods
      3. 16.3 Results and conclusions
      4. Acknowledgment
      5. References
    5. Chapter 17: Protein Tertiary Model Assessment
      1. 17.1 Introduction
      2. 17.2 Overview of Protein Model Assessment
      3. 17.3 Design and Method
      4. 17.4 Implementation Using Svm
      5. 17.5 Implementation Using IFID3
      6. 17.6 Conclusion
      7. References
      8. Bibliography
  10. Part IV: Protein–Protein Analysis of Biological Networks
    1. Chapter 18: Network Algorithms For Protein Interactions
      1. 18.1 Introduction
      2. 18.2 Optimization approaches to clustering
      3. 18.3 Hierarchical algorithms
      4. 18.4 Features of PPI networks
      5. 18.5 Implementation of hierarchical methods
      6. 18.6 Conclusion
      7. References
    2. Chapter 19: Identifying Protein Complexes from Protein–Protein Interaction Networks
      1. 19.1 Introduction
      2. 19.2 Density-Based and Local Search Methods
      3. 19.3 Hierarchical Clustering Methods
      4. 19.4 Finding Overlapping Clusters
      5. 19.5 Identification of Protein Complexes by Integrating Multiple Biological Sources
      6. 19.6 Identifying Protein Complexes From Dynamic PPI Network
      7. 19.7 Challenges and Future Research
      8. References
    3. Chapter 20: Protein Functional Module Analysis With Protein–Protein Interaction (PPI) Networks
      1. 20.1 Introduction
      2. 20.2 Properties of PPI Networks
      3. 20.3 Previous Module Detection Approaches
      4. 20.4 Weighted Graph Model of Protein Interaction Networks
      5. 20.5 Theories and Methods
      6. 20.6 Experimental Results
      7. 20.7 Conclusion
      8. References
    4. Chapter 21: Efficient Alignments of Metabolic Networks with Bounded Treewidth
      1. 21.1 Introduction
      2. 21.2 An overview of metabolic network alignment and mining approaches
      3. 21.3 Generalized Network Alignment Problem
      4. 21.4 A generalized dynamic programming algorithm
      5. 21.5 Predicting pathway holes and resolving enzyme ambiguity
      6. References
    5. Chapter 22: Protein–protein Interaction Network Alignment: Algorithms and Tools
      1. 22.1 Introduction
      2. 22.2 Preliminaries
      3. 22.3 METHODS (Point 5)
      4. 22.4 Coarse-Grain Comparison
      5. 22.5 Concluding Remarks
      6. References
  11. Part V: Application of Protein Bioinformatics
    1. Chapter 23: Protein-Related Drug Activity Comparison Using Support Vector Machines
      1. 23.1 Introduction
      2. 23.2 Related Studies for Pyrimidines Drug Activity Comparison
      3. 23.3 Feature Granules and Hierarchical Kernel Design
      4. 23.4 Experimental Results for Different Machine Learning Models
      5. 23.5 Summary
      6. References
    2. Chapter 24: Finding repetitions in biological networks: challenges, trends, and applications
      1. 24.1 Introduction
      2. 24.2 The Biological Networks Domain
      3. 24.3 Problem Formulation
      4. 24.4 Methods
      5. 24.5 Concluding Remarks
      6. References
    3. Chapter 25: MeTaDoR: Online Resource and Prediction Server for Membrane Targeting Peripheral Proteins
      1. 25.1 Introduction
      2. 25.2 Resource Content
      3. 25.3 Summary and Conclusion
      4. Acknowledgment
      5. References
    4. Chapter 26: Biological networks–based analysis of gene expression signatures*
      1. 26.1 Introduction
      2. 26.2 Gene expression signatures
      3. 26.3 Biological Network–based identification of gene expression signatures
      4. 26.4 Biological Network–based integration of gene expression signatures
      5. 26.5 Discussion and Conclusion
      6. References
  12. Index
  13. Series

Product information

  • Title: Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics
  • Author(s):
  • Release date: November 2013
  • Publisher(s): Wiley-IEEE Press
  • ISBN: 9781118345788