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High-Throughput Identification of Chemistry in Life Science Texts

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Computational Life Sciences II (CompLife 2006)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4216))

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Abstract

OSCAR3 is an open extensible system for the automated annotation of chemistry in scientific articles, which can process thousands of articles per hour. This XML annotation supports applications such as interactive browsing and chemically-aware searching, and has been designed for integration with larger text-analysis systems. We report its application to the high-throughput analysis of the small-molecule chemistry content of texts in life sciences, such as PubMed abstracts.

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Author information

Authors and Affiliations

  1. Unilever center for Moleclular Sciences Informatics, Lensfield Road, Cambridge, CB2 1EW

    Peter Corbett & Peter Murray-Rust

Authors
  1. Peter Corbett
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  2. Peter Murray-Rust
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Editor information

Editors and Affiliations

  1. Berkeley Initiative in Soft Computing (BISC), University of California at Berkeley, USA

    Michael R. Berthold

  2. Department of Chemistry, Unilever Centre for Molecular Informatics, Cambridge University, CB2 1EW, Cambridge, UK

    Robert C. Glen

  3. ALTANA Chair for Bioinformatics and Information Mining, University of Konstanz, Germany

    Ingrid Fischer

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© 2006 Springer-Verlag Berlin Heidelberg

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Corbett, P., Murray-Rust, P. (2006). High-Throughput Identification of Chemistry in Life Science Texts. In: R. Berthold, M., Glen, R.C., Fischer, I. (eds) Computational Life Sciences II. CompLife 2006. Lecture Notes in Computer Science(), vol 4216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875741_11

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  • DOI: https://doi.org/10.1007/11875741_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45767-1

  • Online ISBN: 978-3-540-45768-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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