Data Mining for Bioinformatics / by Sumeet Dua,Pradeep Chowriappa. -- Boca Raton : CRC Press, Taylor & Francis Group, LLC, 2013. – (58.17115/D812 /)

Contents

About the Authors

SECTION I

1  Introduction to Bioinformatics

    1.1   Introduction

    1.2  Transcription and Translation

    1.3   The Human Genome Project

    1.4   Beyond the Human Genome Project

    1.5   Conclusion

    References

2  Biological Databases and Integration

    2.1   Introduction: Scientific Work Flows and Knowledge Discovery

    2.2   Biological Data Storage and Analysis

    2.3   The Curse of Dimensionality

    2.4   Data Cleaning

    2.5   Conclusion

    References

3  Knowledge Discovery in Databases

    3.1   Introduction

    3.2  Analysis of Data Using Large Databases

    3.3   Challenges in Data Cleaning

    3.4  Data Integration

    3.5   Data Warehousing

    3.6   Conclusion

    References

SECTION II

4  Feature Selection and Extraction Strategies in Data Mining

    4.1   Introduction

    4.2  Overfitting

    4.3   Data Transformation

    4.4   Features and Relevance

    4.5   Overview of Feature Selection

    4.6   Filter Approaches for Feature Selection

    4.7   Feature Subset Selection Using Forward Selection

    4.8   Other Nested Subset Selection Methods

    4.9   Feature Construction and Extraction

    4.10  Conclusion

    References

5  Feature Interpretation for Biological Learning

    5.1   Introduction

    5.2   Normalization Techniques for Gene Expression Analysis

    5.3   Data Preprocessing of Mass Spectrometry Data

    5.4   Data Preprocessing for Genomic Sequence Data

    5.5   Ontologies in Bioinformatics

    5.6   Conclusion

    References

SECTION III

6  Clustering Techniques in Bioinformatics

    6.1   Introduction

    6.2   Clustering in Bioinformatics

    6.3   Clustering Techniques

    6.4  Applications of Distance-Based Clustering in Bioinformatics

    6.5   Implementation of k-Means in WEKA

    6.6   Hierarchical Clustering

    6.7   Implementation of Hierarchical Clustering

    6.8   Self-Organizing Maps Clustering

    6.9   Fuzzy Clustering

    6.10 Implementation of Expectation Maximization Algorithm

    6.11  Conclusion

    References

7  Advanced Clustering Techniques

    7.1   Graph-Based Clustering

    7.2   Measures for Identifying Clusters

7.3   Determining a Split in the Graph

7.4   Graph-Based Algorithms

7.5   Application of Graph-Based Clustering in Bioinformatics

7.6   Kernel-Based Clustering

7.7  Application of Kernel Clustering in Bioinformatics

7.8   Model-Based Clustering for Gene Expression Data

7.9   Relevant Number of Genes

7.10  Higher-Order Mining

7.11  Conclusion

References

SECTION IV

8  Classification Techniques in Bioinformatics

    8.1   Introduction

    8.2   Supervised Learning in Bioinformatics

    8.3   Support Vector Machines (SVMs)

    8.4   Bayesian Approaches

    8.5   Decision Trees

    8.6   Ensemble Approaches  .

    8.7   Computational Challenges of Supervised Learning

    8.8   Conclusion

    References

9  Validation and Benchmarking

    9.1   Introduction: Performance Evaluation Techniques

    9.2   Classifier Validation

    9.3   Performance Measures

    9.4   Cluster Validation Techniques

    9.5   Conclusion

    References

Index