Data Mining in Biomedicine Using Ontologies (Artech House Series Bioinformatics & Biomedical Imaging)

279 Pages·2009·4.18 MB·English

by  Mihail Popescu

Data Mining in Biomedicine
Using Ontologies
Artech House Series
Bioinformatics & Biomedical Imaging
Series Editors
Stephen T. C. Wong, The Methodist Hospital and Weill Cornell Medical College
Guang-Zhong Yang, Imperial College
Advances in Diagnostic and Therapeutic Ultrasound Imaging, Jasjit S. Suri,
Chirinjeev Kathuria, Ruey-Feng Chang, Filippo Molinari,
and Aaron Fenster, editors
Biological Database Modeling, Jake Chen and Amandeep S. Sidhu, editors
Biomedical Informatics in Translational Research, Hai Hu, Michael Liebman,
and Richard Mural
Data Mining in Biomedicine Using Ontologies, Mihail Popescu and
Dong Xu, editors
Genome Sequencing Technology and Algorithms, Sun Kim, Haixu Tang,
and Elaine R. Mardis, editors
High-Throughput Image Reconstruction and Analysis, A. Ravishankar Rao
and Guillermo A. Cecchi, editors
Life Science Automation Fundamentals and Applications, Mingjun Zhang,
Bradley Nelson, and Robin Felder, editors
Microscopic Image Analysis for Life Science Applications, Jens Rittscher,
Stephen T. C. Wong, and Raghu Machiraju, editors
Next Generation Artifi cial Vision Systems: Reverse Engineering the Human
Visual System, Maria Petrou and Anil Bharath, editors
Systems Bioinformatics: An Engineering Case-Based Approach, Gil Alterovitz
and Marco F. Ramoni, editors
Text Mining for Biology and Biomedicine, Sophia Ananiadou and
John McNaught, editors
Translational Multimodality Optical Imaging, Fred S. Azar and
Xavier Intes, editors
Data Mining in Biomedicine
Using Ontologies
Mihail Popescu
Dong Xu
Editors
Library of Congress Cataloging-in-Publication Data
A catalog record for this book is available from the U.S. Library of Congress.
British Library Cataloguing in Publication Data
A catalog record for this book is available from the British Library.
ISBN-13: 978-1-59693-370-5
Cover design by Igor Valdman
© 2009 Artech House
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10 9 8 7 6 5 4 3 2 1
Contents
Foreword xi
Preface xiii
CHAPTER 1
Introduction to Ontologies 1
1.1 Introduction 1
1.2 History of Ontologies in Biomedicine 2
1.2.1 The Philosophical Connection 2
1.2.2 Recent Defi nition in Computer Science 2
1.2.3 Origins of Bio-Ontologies 3
1.2.4 Clinical and Medical Terminologies 4
1.2.5 Recent Advances in Computer Science 4
1.3 Form and Function of Ontologies 5
1.3.1 Basic Components of Ontologies 5
1.3.2 Components for Humans, Components for Computers 6
1.3.3 Ontology Engineering 7
1.4 Encoding Ontologies 7
1.4.1 The OBO Format and the OBO Consortium 7
1.4.2 OBO-Edit—The Open Biomedical Ontologies Editor 9
1.4.3 OWL and RDF/XML 9
1.4.4 Protégé—An OWL Ontology Editor 10
1.5 Spotlight on GO and UMLS 10
1.5.1 The Gene Ontology 11
1.5.2 The Unifi ed Medical Language System 12
1.6 Types and Examples of Ontologies 13
1.6.1 Upper Ontologies 14
1.6.2 Domain Ontologies 14
1.6.3 Formal Ontologies 15
1.6.4 Informal Ontologies 15
1.6.5 Reference Ontologies 16
1.6.6 Application Ontologies 16
1.6.7 Bio-Ontologies 17
1.7 Conclusion 17
References 18
v
vi Contents
CHAPTER 2
Ontological Similarity Measures 23
2.1 Introduction 23
2.1.1 History 25
2.1.2 Tversky’s Parameterized Ratio Model of Similarity 27
2.1.3 Aggregation in Similarity Assessment 28
2.2 Traditional Approaches to Ontological Similarity 30
2.2.1 Path-Based Measures 30
2.2.2 Information Content Measures 32
2.2.3 A Relationship Between Path-Based and Information-Content
Measures 35
2.3 New Approaches to Ontological Similarity 36
2.3.1 Entity Class Similarity in Ontologies 36
2.3.2 Cross-Ontological Similarity Measures 37
2.3.3 Exploiting Common Disjunctive Ancestors 38
2.4 Conclusion 39
References 40
CHAPTER 3
Clustering with Ontologies 45
3.1 Introduction 45
3.2 Relational Fuzzy C-Means (NERFCM) 47
3.3 Correlation Cluster Validity (CCV) 49
3.4 Ontological SOM (OSOM) 50
3.5 Examples of NERFCM, CCV, and OSOM Applications 52
3.5.1 Test Dataset 52
3.5.2 Clustering of the GPD Dataset Using NERFCM 53
194
3.5.3 Determining the Number of Clusters of GPD Dataset
194
Using CCV 54
3.5.4 GPD Analysis Using OSOM 56
194
3.6 Conclusion 59
References 60
CHAPTER 4
Analyzing and Classifying Protein Family Data Using OWL Reasoning 63
4.1 Introduction 63
4.1.1 Analyzing Sequence Data 64
4.1.2 The Protein Phosphatase Family 65
4.2 Methods 66
4.2.1 The Phosphatase Classifi cation Pipeline 66
4.2.2 The Datasets 66
4.2.3 The Phosphatase Ontology 67
Contents vii
4.3 Results 70
4.3.1 Protein Phosphatases in Humans 70
4.3.2 Results from the Analysis of A. Fumigatus 71
4.3.3 Ontology System Versus A. Fumigatus Automated Annotation
Pipeline 72
4.4 Ontology Classifi cation in the Comparative Analysis of Three
Protozoan Parasites—A Case Study 74
4.4.1 TriTryps Diseases 74
4.4.2 TriTryps Protein Phosphatases 74
4.4.3 Methods for the Protozoan Parasites 75
4.4.4 Sequence Analysis Results from the TriTryps Phosphatome Study 75
4.4.5 Evaluation of the Ontology Classifi cation Method 77
4.5 Conclusion 78
References 79
CHAPTER 5
GO-Based Gene Function and Network Characterization 83
5.1 Introduction 83
5.2 GO-Based Functional Similarity 84
5.2.1 GO Index-Based Functional Similarity 84
5.2.2 GO Semantic Similarity 85
5.3 Functional Relationship and High-Throughput Data 86
5.3.1 Gene-Gene Relationship Revealed in Microarray Data 86
5.3.2 The Relation Between Functional and Sequence Similarity 87
5.4 Theoretical Basis for Building Relationship Among Genes Through Data 87
5.4.1 Building the Relationship Among Genes Using One Dataset 87
5.4.2 Meta-Analysis of Microarray Data 89
5.4.3 Function Learning from Data 90
5.4.4 Functional-Linkage Network 92
5.5 Function-Prediction Algorithms 93
5.5.1 Local Prediction 93
5.5.2 Global Prediction Using a Boltzmann Machine 95
5.6 Gene Function-Prediction Experiments 98
5.6.1 Data Processing 98
5.6.2 Sequence-Based Prediction 98
5.6.3 Meta-Analysis of Yeast Microarray Data 99
5.6.4 Case Study: Sin1 and PCBP2 Interactions 101
5.7 Transcription Network Feature Analysis 103
5.7.1 Time Delay in Transcriptional Regulation 104
5.7.2 Kinetic Model for Time Series Microarray 104
5.7.3 Regulatory Network Reconstruction 105
5.7.4 GO-Enrichment Analysis 106
5.8 Software Implementation 107
5.8.1 GENEFAS 107
viii Contents
5.8.2 Tools for Meta-Analysis 107
5.9 Conclusion 107
Acknowledgements 108
References 108
CHAPTER 6
Mapping Genes to Biological Pathways Using Ontological
Fuzzy Rule Systems 113
6.1 Rule-Based Representation in Biomedical Applications 113
6.2 Ontological Similarity as a Fuzzy Membership 115
6.3 Ontological Fuzzy Rule System (OFRS) 117
6.4 Application of OFRSs: Mapping Genes to Biological Pathways 120
6.4.1 Mapping Gene to Pathways Using a Disjunctive OFRS 121
6.4.2 Mapping Genes to Pathways Using an OFRS in an
Evolutionary Framework 127
6.5 Conclusion 131
Acknowledgments 131
References 131
CHAPTER 7
Extracting Biological Knowledge by Association Rule Mining 133
7.1 Association Rule Mining and Fuzzy Association Rule Mining Overview 133
7.1.1 Association Rules: Formal Defi nition 134
7.1.2 Association Rule Mining Algorithms 137
7.1.3 Apriori Algorithm 138
7.1.4 Fuzzy Association Rules 140
7.2 Using GO in Association Rule Mining 144
7.2.1 Unveiling Biological Associations by Extracting Rules Involving
GO Terms 144
7.2.2 Giving Biological Signifi cance to Rule Sets by Using GO 147
7.2.3 Other Joint Applications of Association Rules and GO 150
7.3 Applications for Extracting Knowledge from Microarray Data 152
7.3.1 Association Rules That Relate Gene Expression Patterns with
Other Features 153
7.3.2 Association Rules to Obtain Relations Between Genes and
Their Expression Values 155
Acknowledgements 157
References 157
CHAPTER 8
Text Summarization Using Ontologies 163
8.1 Introduction 163
8.2 Representing Background Knowledge—Ontology 164
8.2.1 An Algebraic Approach to Ontologies 165
Contents ix
8.2.2 Modeling Ontologies 166
8.2.3 Deriving Similarity 167
8.3 Referencing the Background Knowledge—Providing Descriptions 167
8.3.1 Instantiated Ontology 170
8.4 Data Summarization Through Background Knowledge 173
8.4.1 Connectivity Clustering 173
8.4.2 Similarity Clustering 177
8.5 Conclusion 181
References 182
CHAPTER 9
Reasoning over Anatomical Ontologies 185
9.1 Why Reasoning Matters 185
9.2 Data, Reasoning, and a New Frontier 187
9.2.1 A Taxonomy of Data and Reasoning 187
9.2.2 Contemporary Reasoners 189
9.2.3 Anatomy as a New Frontier for Biological Reasoners 193
9.3 Biological Ontologies Today 195
9.3.1 Current Practices 195
9.3.2 Structural Issues That Limit Reasoning 196
9.3.3 A Biological Example: The Maize Tassel 197
9.3.4 Representational Issues 199
9.4 Facilitating Reasoning About Anatomy 205
9.4.1 Link Different Kinds of Knowledge 206
9.4.2 Layer on Top of the Ontology 206
9.4.3 Change the Representation 207
9.5 Some Visions for the Future 208
Acknowledgments 208
References 209
CHAPTER 10
Ontology Applications in Text Mining 219
10.1 Introduction 219
10.1.1 What Is Text Mining? 219
10.1.2 Ontologies 220
10.2 The Importance of Ontology to Text Mining 220
10.3 Semantic Document Clustering and Summarization: Ontology
Applications in Text Mining 222
10.3.1 Introduction to Document Clustering 222
10.3.2 The Graphical Representation Model 223
10.3.3 Graph Clustering for Graphical Representations 228
10.3.4 Text Summarization 230
10.3.5 Document Clustering and Summarization with Graphical
Representation 233
10.4 Swanson’s Undiscovered Public Knowledge (UDPK) 235