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Extracting Information for Meaningful Function Inference through Text-Mining

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Discovering Biomolecular Mechanisms with Computational Biology

Part of the book series: Molecular Biology Intelligence Unit ((MBIU))

Abstract

One of the emerging technologies in computational biology is text-mining which includes natural language processing. This technology enables extraction of parts of relevant biological knowledge from a large volume of scientific documents in an automated fashion. We present several systems which cover different facets of text-mining biological information with applications in transcription control, metabolic pathways, and bacterial cross-species comparison. We demonstrate how this technology can efficiently support biologists and medical scientists to infer function of biological entities and save them a lot of time, paving way for more focused and detailed follow-up research.

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Author information

Authors and Affiliations

  1. Knowledge Extraction Lab, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, 119613, Singapore

    Hong Pan, Li Zuo, Rajaraman Kanagasabai, Zhuo Zhang, Vidhu Choudhary, Bijayalaxmi Mohanty, Sin Lam Tan, S. P. T. Krishnan & Vladimir B. Bajic

  2. Department of Biological Sciences, National University of Singapore, Singapore

    Pardha Sarathi Veladandi, Archana Meka, Weng Keong Choy & Sanjay Swarup

Authors
  1. Hong Pan
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  2. Li Zuo
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  3. Rajaraman Kanagasabai
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  4. Zhuo Zhang
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  5. Vidhu Choudhary
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  6. Bijayalaxmi Mohanty
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  7. Sin Lam Tan
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  8. S. P. T. Krishnan
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  9. Pardha Sarathi Veladandi
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  11. Weng Keong Choy
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  13. Vladimir B. Bajic
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© 2006 Landes Bioscience and Springer Science+Business Media

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Pan, H. et al. (2006). Extracting Information for Meaningful Function Inference through Text-Mining. In: Discovering Biomolecular Mechanisms with Computational Biology. Molecular Biology Intelligence Unit. Springer, Boston, MA. https://doi.org/10.1007/0-387-36747-0_5

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