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Methods in Biomedical Informatics
A Pragmatic Approach
- 1st Edition - September 3, 2013
- Editor: Indra Neil Sarkar
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 4 0 1 6 7 8 - 1
- eBook ISBN:9 7 8 - 0 - 1 2 - 4 0 1 6 8 4 - 2
Beginning with a survey of fundamental concepts associated with data integration, knowledge representation, and hypothesis generation from heterogeneous data sets, Methods in Biome… Read more
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Request a sales quoteBeginning 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.
Contributors
Chapter 1. Introduction
Abstract
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
Chapter 2. Data Integration: An Overview
Abstract
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
References
Chapter 3. Knowledge Representation
Abstract
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
3.6 Summary
References
Chapter 4. Hypothesis Generation from Heterogeneous Datasets
Abstract
Acknowledgments
4.1 Introduction
4.2 Preliminary Background
4.3 Description of Methods
4.4 Applications in Medicine and Public Health
4.5 Summary
References
Chapter 5. Geometric Representations in Biomedical Informatics: Applications in Automated Text Analysis
Abstract
5.1 Introduction
5.2 The Nature of Geometric Representations
5.3 Dimension Reduction
5.4 Classification
5.5 Beyond Distance
5.6 Building Geometric Models with the Semantic Vectors Package
5.7 Summary and Conclusion
References
Chapter 6. Biomedical Natural Language Processing and Text Mining
Abstract
Acknowledgments
6.1 Natural Language Processing and Text Mining Defined
6.2 Natural Language
6.3 Approaches to Natural Language Processing and Text Mining
6.4 Some Special Considerations of Natural Language Processing in the Biomedical Domain
6.5 Building Blocks of Natural Language Processing Applications
6.6 Additional Components of Natural Language Processing Applications
6.7 Evaluation in Natural Language Processing
6.8 Practical Applications: Text Mining Tasks
6.9 Software Engineering in Natural Language Processing
6.10 Conclusion
References
Chapter 7. Knowledge Discovery in Biomedical Data: Theory and Methods
Abstract
7.1 Introduction
7.2 Knowledge Discovery as a Process: Data Mining in Perspective
7.3 A Brief Overview of Machine Learning
7.4 A Knowledge Discovery Life Cycle
7.5 Ethical Issues
7.6 Summary
7.7 Additional Resources
References
Chapter 8. Bayesian Methods in Biomedical Data Analysis
Abstract
8.1 Introduction
8.2 Fundamentals of Bayesian Methods
8.3 Bayesian Network Analysis
8.4 Biomedical Applications
8.5 Summary
References
Chapter 9. Learning Classifier Systems: The Rise of Genetics-Based Machine Learning in Biomedical Data Mining
Abstract
9.1 Introduction
9.2 Learning Classifier Systems
9.3 Facing the Challenges
9.4 Rise of the Machines
References
Chapter 10. Engineering Principles in Biomedical Informatics
Abstract
10.1 Introduction
10.2 Building Innovative Products: Implications for Biomedical Informatics
10.3 Modeling Information Flows for Software Engineering: The Unified Modeling Language
10.4 UML Applications in Biomedical Informatics
10.5 From Modeling to Simulation: Careflow Representation, Simulation, and Learning
10.6 Engineering Principles and Ideas in Biomedical Data Mining
10.7 Building and Evaluating Data Mining Models
10.8 Discussion
10.9 Conclusions
References
Chapter 11. Biomedical Informatics Methods for Personalized Medicine and Participatory Health
Abstract
11.1 Introduction to Personalized Medicine
11.2 Data Sources for Personalized Medicine
11.3 Data Analysis for Personalized Medicine
11.4 Introduction to Participatory Health
11.5 Data Collection for Participatory Health
11.6 Data Exchange and Use in Participatory Health
11.7 Conclusions and Future Directions
References
Chapter 12. Linking Genomic and Clinical Data for Discovery and Personalized Care
Abstract
12.1 Introduction
12.2 Repurposing EHRs for Clinical and Translational Research
12.3 Phenotyping Algorithms: Finding Meaningful Populations of Patients from EHR Data
12.4 Types of Genetic Association Studies
12.5 Moving to the Bedside: Implementing Genetics into the EHR
12.6 Summary
12.7 Selected Websites of Interest
References
Further reading
Chapter 13. Putting Theory into Practice
Abstract
13.1 Entering the Era of Big Data
13.2 Prospective Applicability of Biomedical Informatics Methodologies
13.3 A Final Challenge to the Reader
References
Appendix A. Unix Primer
A.1 Unix File System
A.2 Unix Shell
A.3 Unix Command Syntax
A.4 Basic Commands
A.5 Traversing Directories and Files
A.6 Working with Directories and Files
A.7 Input and Output
A.8 Basic Data Analysis
A.9 Access Permissions
A.10 Text Editors
A.11 Summary of Commands
References and Resources
History
Commands
Text Editors
Appendix B. Ruby Primer
B.1 Hello World
B.2 Extensions to Hello World
B.3 Numbers and Math
B.4 Strings
B.5 Conditional Statements and Control Structures
B.6 Directories and Files
B.7 Regular Expressions
B.8 Arrays and Hashes
B.9 Interactive Ruby (irb)
B.10 Ruby Gems
References and Resources
Books and Articles
Web Resources and Tutorials
Appendix C. Database Primer
C.1 Example PubMed/MEDLINE Database
C.2 Working in the MySQL Environment
C.3 Creating, Accessing, and Deleting a Database
C.4 Creating, Modifying, and Deleting Tables
C.5 Loading Data into Tables
C.6 Retrieving Data from Tables
C.7 Joining Multiple Tables
C.8 Automating MySQL Queries
References and Resources
Articles
Web Resources and Tutorials
Appendix D. Web Services
D.1 NCBI E-utilities
D.2 NCBO Annotator
References and Resources
Book and Articles
Web Resources and Tutorials
Index
Subject Index
- No. of pages: 592
- Language: English
- Edition: 1
- Published: September 3, 2013
- Imprint: Academic Press
- Hardback ISBN: 9780124016781
- eBook ISBN: 9780124016842
IS
Indra Neil Sarkar
Affiliations and expertise
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.Read Methods in Biomedical Informatics on ScienceDirect