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European Pharmaceutical Contractor

Text Mining for Drug Discovery

An increasingly prominent topic at conferences of late concerns the pros and cons of text mining and how to extract the full value of the literature for drug discovery. There have been a few false starts and misunderstandings about what text mining is and what it can do. As with many new technologies, some of the claims are somewhat excessive, yet we predict that text mining will join many others in drug discovery as a key enabling technology.

What is text mining? At a basic level, text mining is the process of highlighting a small volume of relevant information from a very large set of possibly interesting documents. There is nothing magical about the process; text mining software's 'understanding' of the literature is still rather rudimentary. However, while it may make mistakes where a researcher would recognise the interpretation as wrong, or even stupid, it is generally reliable. And unlike analysts who get tired, its actions are consistent. The greatest benefit of software in analysing the literature is that it can slog through amounts of text that no person could ever hope to manage. The resulting information can then be used directly by humans or as an input in a follow-on data mining exercise.


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By James Fickett, PhD, Director, Strategic Capabilities, Discovery IS and Informatics, and William Hayes, PhD, Global Cross-Discovery and External Technology Strategic Capability Manager for AstraZeneca

James Fickett, PhD, is Director of Strategic Capabilities, Discovery IS and Informatics, at Astra Zeneca. James entered the field of bioinformatics in 1980 as one of the founders of GenBank. He was active in the development of sequence analysis algorithms for many years, pioneering the development of algorithms for gene structure prediction and analysis of transcriptional regulatory regions. He joined SmithKline Beecham in 1996 and headed the Bioinformatics Research Group there. He now heads scientific and strategic aspects of IS and informatics for AstraZeneca. James received a BA in Spanish, Portuguese and Mathematics from University of Arizona in 1974, and holds a PhD in Mathematics from University of Colorado.


William Hayes, PhD, is the Global Cross-Discovery and External Technology Strategic Capability Manager for AstraZeneca. He has extensive experience with text mining, bioinformatics and grid computing in meeting the challenges of informatics-driven drug discovery. His interest in text mining grew from his observations while working with a large-scale gene promoter analysis project where time in the library had greatly overtaken computational analyses.


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