While many text mining projects emphasize retrieval and extraction,
text mining can be leveraged to discover new and previously unknown
information. Nowhere is the potential more apparent than in
pharmacogenomics-based drug discovery. Text mining can help
pharmaceutical researchers reduce the vast information overload
hindering pharmacogenomics-based drug discovery because it can aid
in the generation of rich novel information from large collections
of diverse scientific literature and research data. However the
pharmaceutical industry appears to be reluctant to innovate
bleeding-edge text mining technologies for drug discovery. The
present book re-frames text mining as an approach to automate the
generation of novel and interesting information, reviews successful
exemplary text mining applications, and examines a case study of a
leading pharmaceutical company within the book's proposed
novelty-generation paradigm. The present book is written for a wide
range of professionals and scholars, not only for information
scientists, industry analysts, and pharmaceutical executives, but
also for those interested in innovation studies and the automated
acceleration of discovery.
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