Methods in Biomedical Informatics

Methods in Biomedical Informatics, 1st Edition

A Pragmatic Approach

Methods in Biomedical Informatics, 1st Edition,Indra Neil Sarkar,ISBN9780124016781

I Sarkar   

Academic Press




235 X 191

Foundational background of key methodological principles in biomedical informatics and their specific applications in biology, medicine and public health

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Key Features

  • 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.


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.


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.

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.

Methods in Biomedical Informatics, 1st Edition


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

3.6 Summary


Chapter 4. Hypothesis Generation from Heterogeneous Datasets



4.1 Introduction

4.2 Preliminary Background

4.3 Description of Methods

4.4 Applications in Medicine and Public Health

4.5 Summary


Chapter 5. Geometric Representations in Biomedical Informatics: Applications in Automated Text Analysis


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


Chapter 6. Biomedical Natural Language Processing and Text Mining



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


Chapter 7. Knowledge Discovery in Biomedical Data: Theory and Methods


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


Chapter 8. Bayesian Methods in Biomedical Data Analysis


8.1 Introduction

8.2 Fundamentals of Bayesian Methods

8.3 Bayesian Network Analysis

8.4 Biomedical Applications

8.5 Summary


Chapter 9. Learning Classifier Systems: The Rise of Genetics-Based Machine Learning in Biomedical Data Mining


9.1 Introduction

9.2 Learning Classifier Systems

9.3 Facing the Challenges

9.4 Rise of the Machines


Chapter 10. Engineering Principles in Biomedical Informatics


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


Chapter 11. Biomedical Informatics Methods for Personalized Medicine and Participatory Health


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


Chapter 12. Linking Genomic and Clinical Data for Discovery and Personalized Care


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


Further reading

Chapter 13. Putting Theory into Practice


13.1 Entering the Era of Big Data

13.2 Prospective Applicability of Biomedical Informatics Methodologies

13.3 A Final Challenge to the Reader


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



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


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


Subject Index

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