Methods In Biomedical Informatics/A pragmatic Approach Indra Neil Sarkar

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index

CONTENTS
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
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 3 Knowledge Representation
3.1 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
4.2.4 Text Mining for Knowledge Discovery
4.2.5 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 4.3.3.2.1 Statistical Fusion
4.3.3.2.2 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

:Seeking to cross the bridge among overview, theory, and practice, this book incorporates both methodological approaches and their potential application in the domains associated with biomedical informatics. The multi-contributor book is useful for (1) those coming from a domain seeking biomedical informatics approaches for addressing specific needs; and, (2) current biomedical informaticians seeking a foundational background for methods that might be utilized in practical scenarios germane to their ongoing research. A unique characteristic of the text is its balance between foundational coverage of core topics in biomedical informatics with practical "in-the-trenches" scenarios. Contributors represent leading experts from the biomedical informatics field: individuals who have demonstrated effective use of methodology in real-world, high-quality data applications. Contains appendices that function as primers on: (1) Unix; (2) Ruby; (3) Databases; and (4) Web Services