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Mining the Biomedical Literature

Hagit Shatkay and Mark Craven

Publication Year: 2012

A concise introduction to fundamental methods for finding and extracting relevant information from the ever-increasing amounts of biomedical text available.

Published by: The MIT Press

Title Page, Copyright

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Acknowledgments

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pp. ix-xi

As Selim Akl kindly pointed out at the very early stages of this endeavor, writing a book can be a pretty lonely job. However, as it turned out, our interaction with students and colleagues throughout this time — conversations, discussions, debates, and arguments, both public and private — all helped to...

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1. Introduction

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pp. 1-8

The current millennium started with the sequencing of the human genome. There are now thousands of sequenced genomes available, covering a wide range of organisms and a broad collection of individuals within the human population. Additionally, there is a multitude of data-sets characterizing dynamic...

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2. Fundamental Concepts in Biomedical Text Analysis

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pp. 9-32

The development of the Internet has made it easy for biologists to create databases and online portals representing various aspects of biological knowledge and to make these resources publicly available. Although there are hundreds of such online resources representing biological knowledge in a structured...

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3. Information Retrieval

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pp. 33-52

In its most basic form, information retrieval is the task of finding a set of relevant documents in a large text collection. Naturally, the relevance of a document depends on our particular information need at a given moment. Most of us perform information retrieval on a daily basis, using search engines such as...

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4. Information Extraction

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pp. 53-76

Chapter 3 focused on tasks that involve identifying which documents or passages in a large corpus are relevant to a given query. In some situations, however, one may want automated systems to perform a more fine-grained, in-depth analysis of the text. One type of analysis that may be useful is the identification...

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5. Evaluation

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pp. 77-98

From the discussion in previous chapters, it is clear that automated text mining and effective information retrieval can help realize a wide range of biological and medical goals. These goals vary in scope and domain; some examples of these goals, ordered in ascending level of difficulty, may include the...

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6. Putting It All Together: Current Applications and Future Directions

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pp. 99-114

Throughout the previous chapters we have covered a variety of text-mining methods applicable to the broad range of tasks that are involved in obtaining information from text. In the beginning of chapter 1 we listed several goals within the biomedical domain that can be realized through the use of text. In this...

References

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pp. 115-129

Index

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pp. 131-138


E-ISBN-13: 9780262305167
Print-ISBN-13: 9780262017695

Page Count: 150
Publication Year: 2012

Series Title: Computational Molecular Biology

Research Areas

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Subject Headings

  • Medical literature -- Data processing.
  • Biological literature -- Data processing.
  • Data mining.
  • Medical informatics.
  • Bioinformatics.
  • Information storage and retrieval systems -- Medicine.
  • Information storage and retrieval systems -- Biology.
  • Content analysis (Communication).
  • Information retrieval.
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