Revision with unchanged content. 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.

The author is Research Analyst and Chief Technologistfor the Jenkins Chair in New Technologies and Society at Duke University.