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.
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Weeber, M. (2007). Drug Discovery as an Example of Literature-Based Discovery. In: Džeroski, S., Todorovski, L. (eds) Computational Discovery of Scientific Knowledge. Lecture Notes in Computer Science(), vol 4660. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73920-3_14
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DOI: https://doi.org/10.1007/978-3-540-73920-3_14
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