Drug Discovery as an Example of Literature-Based Discovery

Authors:
Marc Weeber
Affiliations:
Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, USA
Venue:
Computational Discovery of Scientific Knowledge
Year:
2007

Citing 9
Cited 1

Toward discovery support systems: a replication, re-examination, and extension of Swanson's work on literature-based discovery of a connection between Raynaud's and fish oil

Journal of the American Society for Information Science
Scientific discovery and simplicity of method

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An interactive system for finding complementary literatures: a stimulus to scientific discovery

Artificial Intelligence - Special issue on scientific discovery
The enacted fate of undiscovered public knowledge

Journal of the American Society for Information Science
Literature-based discovery by lexical statistics

Journal of the American Society for Information Science
The computational support of scientific discovery

International Journal of Human-Computer Studies - Special issue on Machine Discovery
Using concepts in literature-based discovery: simulating Swanson's Raynaud-fish oil and migraine-magnesium discoveries

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Extracting the lowest-frequency words: pitfalls and possibilities

Computational Linguistics
Principles of human-computer collaboration for knowledge discovery in science

Artificial Intelligence

Bisociative knowledge discovery by literature outlier detection

Bisociative Knowledge Discovery

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Abstract

Since Swanson's introduction of literature-based discovery in 1986, new hypotheses have been generated by connecting disconnected scientific literatures. In this paper, we present the general discovery model and show how it can be used for drug discovery research. We have developed a discovery support tool that employs Natural Language Processing techniques to extract biomedical concepts from Medline titles and abstracts. Using semantic knowledge, the user, typically a biomedical scientist, can efficiently filter out irrelevant information. This chapter provides an algorithmic description of the system and presents a potential drug discovery. We conclude by discussing the current and future status of literature-based discovery in the biomedical research domain.