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Illustrating the power of information in life science research

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  1. Introduction.
    1. The ironic proverbial saying that ?a month in the lab can save you an hour in the library?.
    2. Substantial costs.
    3. The value of information.
    4. A rigorous description of literature-based discovery.
  2. Barriers to the automation of these discoveries.
    1. Finding relevant information.
    2. Getting a citation from PubMed.
  3. Shifting paradigms.
    1. A variety of technological advances.
    2. Project Prospect.
  4. Chemical information mining: A new paradigm.
    1. Computer-assisted extraction.
    2. Textual names.
    3. The use of computer-readable formats for chemical structures.
    4. Chemical identifiers.
  5. Conclusion.

The ironic proverbial saying that ?a month in the lab can save you an hour in the library? is proving itself repeatedly and at a huge cost to both academic and commercial institutions alike. Missed information in the literature costs time, money, and quality. Both the quality of decisions made and the quality of subsequent research output is compromised when the available information is not realized. In monetary terms, incorrect decisions along the drug pipeline lifecycle in the pharmaceutical area can cost millions to billions of dollars.

[...] Chemical information mining: A new paradigm Computer-assisted extraction or mining of chemical structural information from the literature requires special tools that address the various ways of encoding structures. Traditionally, in the literature, chemical structures are identified by textual names or images of structures. Chemical images of structures are in general very explicit and can convey a great deal of information to a chemist, but they cannot be read by computers. To make these images machine-readable would involve a chemical image recognition capability. [...]

[...] This contextual component can be a very simple and powerful research tool that paves the way for a new paradigm in chemical information mining of the literature, using text analytical tools such as chemical name entity recognition (NER) together with natural language processing (NLP). Conclusion Ultimately, the use of these capabilities has to enhance the ways in which researchers in both academics and industry work. Information overload is a major driver in this shifting paradigm, along with a variety of technological advances in other key areas. [...]

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