UID:
almafu_9958086736602883
Umfang:
1 online resource (xvi, 571 pages) :
,
illustrations (some color)
ISBN:
9780124016842
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0124016847
Serie:
Gale eBooks
Inhalt:
Beginning with a survey of fundamental concepts associated with data integration, knowledge representation, and hypothesis generation from heterogeneous data sets, Methods in Biomedical Informatics provides a practical survey of methodologies used in biological, clinical, and public health contexts. These concepts provide the foundation for more advanced topics like information retrieval, natural language processing, Bayesian modeling, and learning classifier systems. The survey of topics then concludes with an exposition of essential methods associated with engineering, personalized
Anmerkung:
Description based upon print version of record.
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Half Title; Title Page; Copyright; Contents; Contributors; 1 Introduction; 1.1 Biomedical Informatics and its Applications; 1.2 The Scientific Method; 1.3 Data, Information, Knowledge, and Wisdom; 1.4 Overview of Chapters; 1.5 Expectations and Challenge to the Reader; References; 2 Data Integration: An Overview; 2.1 Objectives of Integration; 2.2 Integration Approaches: Overview; 2.2.1 Scope of this Chapter; 2.3 Database Basics; 2.3.1 SQL Dialects; 2.3.2 Design for High Performance; 2.3.3 Data Integration vs. Interoperation; 2.4 Physical vs. Logical Integration: Pros and Cons
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2.5 Prerequisite Subtasks2.5.1 Determining Objectives; 2.5.2 Identifying Elements: Understanding the Data Sources; 2.5.2.1 Identifying Redundancy and Inconsistency; 2.5.2.2 Characterizing Heterogeneity: Modeling Conflicts; 2.5.3 Data Quality: Identifying and Fixing Errors; 2.5.4 Documenting Data Sources and Processes: Metadata; 2.5.4.1 Ontologies; 2.6 Data Transformation and Restructuring; 2.7 Integration Efforts in Biomedical Research; 2.8 Implementation Tips; 2.8.1 Query Tools: Caveats; 2.8.2 The Importance of Iterative Processes; 2.9 Conclusion: Final Warnings; References
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3 Knowledge Representation3.1 Knowledge and Knowledge Representation; 3.2 Procedural VS. Declarative Representations; 3.3 Representing Knowledge Declaratively; 3.3.1 Logics; 3.3.2 Semantic Networks; 3.3.3 Frames; 3.3.4 Rules; 3.3.5 Description Logic; 3.4 What Does a Representation Mean?; 3.5 Building Knowledge Bases in Practice; 3.6 Summary; References; 4 Hypothesis Generation from Heterogeneous Datasets; 4.1 Introduction; 4.2 Preliminary Background; 4.2.1 Modeling Biological Structures and Their Interplay; 4.2.2 Data and Knowledge Representation; 4.2.3 Data Format Conversion
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4.2.4 Text Mining for Knowledge Discovery4.2.5 Fundamental Statistical and Computational Methods; 4.3 Description of Methods; 4.3.1 Determination of Study Scales and Associated Simplifying Hypotheses; 4.3.2 Curse of Dimensionality, Classification, and Feature Selection; 4.3.3 Approaches of Integration; 4.3.3.1 Corroborative Approaches; 4.3.3.1.1 Logical Filtering Evidence From Multiple Scales; 4.3.3.1.2 Information Joining From Multiple Datasets; 4.3.3.1.3 Correlation Among Multiple Scales; 4.3.3.1.4 Similarity Measurement Between Datasets; 4.3.3.2 Fusion Approaches
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4.3.3.2.1 Statistical Fusion4.3.3.2.2 Mathematical Fusion; 4.3.3.2.3 Computational Fusion; 4.3.4 Multiple Comparison Adjustments, Empirical and Statistical Controls; 4.4 Applications in Medicine and Public Health; 4.5 Summary; Acknowledgments; References; 5 Geometric Representations in Biomedical Informatics: Applications in Automated Text Analysis; 5.1 Introduction; 5.2 The Nature of Geometric Representations; 5.2.1 Vectors and Vector Spaces; 5.2.2 Distance Metrics; 5.2.3 Examples: Term, Concept, and Document Vectors; 5.2.4 Term-Weighting; 5.2.5 Example: Literature-Based Discovery
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5.2.6 Summary and Implications
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English
Weitere Ausg.:
ISBN 9780124016781
Weitere Ausg.:
ISBN 0124016782
Sprache:
Englisch
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