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More About This Textbook
Overview
This book presents innovative approaches from database researchers supporting the challenging process of knowledge discovery in biomedicine. Ranging from how to effectively store and organize biomedical data via data quality and case studies to sophisticated data mining methods, this book provides the state-of-the-art of database technology for life sciences and medicine.
A valuable source of information for experts in life sciences who want to be updated about the possibilities of database technology in their field, this volume will also be inspiring for students and researchers in informatics who are keen to contribute to this emerging field of interdisciplinary research.
Key Features
Provides a forum to present interdisciplinary research in computer science, life sciences and medicine
Includes top-level peer-reviewed contributions from world-renowned research groups such as Curtin University of Technology (Australia), Emory University (USA), and RWTH (Germany)
Covers the entire knowledge discovery process biological and medical data, including questions of data storage, data selection, data fusion and data mining
Product Details
Table of Contents
Preface v
1 Biomedical Databases and Data Mining 1
1.1 Databases and Knowledge Discovery in Biomedicine 2
1.2 Outline of this Book 5
2 DYNASTAT: A Methodology for Modeling of Multi-agent Systems 11
2.1 Introduction 11
2.2 Literature Review 13
2.3 DYNASTAT Methodology 16
2.4 Use of UML 2.2 in the Framework of DYNASTAT Methodology 18
2.5 UML to Model Medical Multi-agent Systems 23
2.6 Possible Applications 30
2.7 Conclusion 33
3 SciPort: An Extensible Data Management Platform for Biomedical Research 35
3.1 Introduction 35
3.2 Related Work 38
3.3 Unified Scientific Data Modeling 39
3.4 Document Authoring and Searching 47
3.5 Sharing Distributed Biomedical Data 56
3.6 The Architecture of SciPort 59
3.7 Conclusion 62
4 An Integrative Framework for Anonymizing Clinical and Genomic Data 65
4.1 Introduction 65
4.2 Related Work 70
4.3 The DIANOVA Framework 77
4.4 Algorithms for Realizing DIANOVA 83
4.5 Extensions of DIANOVA 88
4.6 Conclusion 88
5 Data Integration Challenges: A Systems Biology Perspective 91
5.1 Introduction 91
5.2 Modeling Biological Systems 92
5.3 Biological and Mathematical Data Sources 94
5.4 Various Data Exchange Formats in Systems Biology 96
5.5 Building an Integrative Framework to Combine Modeling and Biological Data Sources 98
5.6 Analysis of the Developed Integrative Environment 111
5.7 Conclusion 113
6 Ontology-based Data Integration: A Case Study in Clinical Trials 115
6.1 Introduction 115
6.2 System Architecture and Overview 117
6.3 The CTDM Ontology 118
6.4 Ontology-based Data Integration of Study Relevant Information 121
6.5 Assembly of ETL Processes Based on Ontology Mappings 130
6.6 Case Study and Evaluation 132
6.7 Related Work 135
6.8 Conclusion 136
7 A Data Warehouse for Ca. Glomeribacter Gigasporarum Bacterium 139
7.1 Introduction 139
7.2 State-of-the-art of Metagenomics for Genomic Comparison 141
7.3 BIOBITS System Architecture 143
7.4 Software Modules to Support Researchers' Activities 149
7.5 Conclusion 154
8 Quality of Medical Data: A Case Study 157
8.1 Introduction 158
8.2 Case Study 165
8.3 Generation of a Summary Table 172
8.4 Conclusion 173
9 Efficient EMD-based Similarity Search in Medical Image Databases 175
9.1 Introduction 175
9.2 Dimensionality Reduction for the EMD 182
9.3 Query Processing Algorithm 192
9.4 Evaluation on Medical Data Sets 193
9.5 Conclusion 200
10 Fast Multimedia Querying for Medical Applications 203
10.1 Introduction 203
10.2 Related Work 206
10.3 Subspace Tree 208
10.4 Experiments 215
10.5 Conclusion 218
11 Ensemble Feature Selection in Biomedical Applications 221
11.1 Introduction 222
11.2 Evaluation of Feature Selection Approaches 225
11.3 Ensemble Feature Selection 228
11.4 Biomedical Example 230
11.5 Computational Approach 232
11.6 Results 233
11.7 Discussion 235
11.8 Conclusion 238
12 Analysis of Breast Cancer Genomic Data by Fuzzy Association Rule Mining 241
12.1 Introduction 242
12.2 Microarrays 247
12.3 Association Rule Mining 247
12.4 Fuzzy Association Rules 251
12.5 Dataset 254
12.6 Extracting the Fuzzy Association Rules 257
12.7 Results 268
12.8 Conclusion 277
13 Graph Mining on Brain Co-activation Networks 279
13.1 Introduction 281
13.2 Related Work 282
13.3 Method 284
13.4 Experiments 288
13.5 Conclusion 293
14 Automatic Identification of Surgery Indicators 295
14.1 Introduction 295
14.2 Background 297
14.3 Approach 302
14.4 Experiment 308
14.5 Conclusion 318
15 Incremental Learning of Medical Data for Multi-step Patient Health Classification 321
15.1 Introduction 321
15.2 The Bayes Tree 325
15.3 Experimental Evaluation 330
15.4 Incorporating Medical Knowledge Data Bases 337
15.5 Application of Anytime Classification 340
15.6 Conclusion 343
Bibliography 345