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 medicine, and linking of genomic and clinical data. Within an overall context of the scientific method, Methods in Biomedical Informatics provides a practical coverage of topics that is specifically designed for: (1) domain experts seeking an understanding of biomedical informatics approaches for addressing specific methodological needs; or (2) biomedical informaticians seeking an approachable overview of methodologies that can be used in scenarios germane to biomedical research.
- Contributors represent leading biomedical informatics experts: individuals who have demonstrated effective use of biomedical informatics methodologies in the real-world, high-quality biomedical applications
- Material is presented as a balance between foundational coverage of core topics in biomedical informatics with practical "in-the-trenches" scenarios.
- Contains appendices that function as primers on: (1) Unix; (2) Ruby; (3) Databases; and (4) Web Services.
Biomedical informaticians seeking methods that can be used in on-going research, and biological and medical practitioners seeking biomedical informatics approaches to address specific needs.
Chapter 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
Chapter 2. Data Integration: An Overview
2.1 Objectives of Integration
2.2 Integration Approaches: Overview
2.3 Database Basics
2.4 Physical vs. Logical Integration: Pros and Cons
2.5 Prerequisite Subtasks
2.6 Data Transformation and Restructuring
2.7 Integration Efforts in Biomedical Research
2.8 Implementation Tips
2.9 Conclusion: Final Warnings
Chapter 3. Knowledge Representation
3.1 Knowledge and Knowledge Representation
3.2 Procedural VS. Declarative Representations
3.3 Representing Knowledge Declaratively
3.4 What Does a Representation Mean?
3.5 Building Knowledge Bases in Practice
Chapter 4. Hypothesis Generation from Heterogeneous Datasets
4.2 Preliminary Background
4.3 Description of Methods
4.4 Applications in Medicine and Public Health
Chapter 5. Geometric Representations in Biomedical Informatics: Applications in Automated Text Analysis
5.2 The Nature of Geometric Representations
5.3 Dimension Reduction
5.5 Beyond Distance
5.6 Building Geometric Models with the Semantic Vectors Package
5.7 Summary and Conclusion
Chapter 6. Biomedical Natural Language Processing and Text Mining
6.1 Natural Language Processing
- No. of pages:
- © Academic Press 2014
- 3rd October 2013
- Academic Press
- eBook ISBN:
- Hardcover ISBN:
Director of Biomedical Informatics and Assistant Professor in Microbiology and Molecular Genetics (with a Secondary Appointment in Computer Science) at the University of Vermont, Burlington, Vermont.