Description

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.

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.

Readership

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.

Table of Contents

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

Details

No. of pages:
592
Language:
English
Copyright:
© 2014
Published:
Imprint:
Academic Press
Print ISBN:
9780124016781
Electronic ISBN:
9780124016842

About the author

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.