Machete: Charting Excursions through Bioscience Literature

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Machete Charting Excursions through Bioscience
Literature

• Shannon Bradshaw1 and Marc Light2
• 1Department of Management Sciences
• 2School of Library and Information Science
• 2Department of Linguistics
• 1,2Department of Computer Science
• The University of Iowa

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Motivation

• In Biology, 40,000 new articles are published
  each month (Fontanelo 2004)
• Biologists spend as much as 20 of their time
  gathering information (Hayes 2004)
• For evolutionary biologists, the problem is
  especially severe
• A gene or protein of interest may lead them to
  organisms (areas of the literature) with which
  they have no familiarity

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Real-world example

• In recent wet lab work a graduate student
  discovered proteins called ankyrins in the
  organism he was studying
• Learning the function of ankyrins and what is
  known about ankyrin repeats was critical to his
  research
• He performed a time-consuming web/literature
  search to uncover in what organisms these
  proteins occur and what is known about them

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Personal Information Management

• Problem It is difficult for biologists to
  assemble needed information and save it for later
  review
• Approach Developing a PIM application that
  enables users to
• Assemble and organize information addressing
  specific information needs
• Save it locally
• Make it searchable

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Text Mining

• Problem
• Too many relevant articles to sift through
• It is frequently not an entire article that an
  investigator needs but a sentence or paragraph
  within that article
• Approach Developing text mining tools that allow
  the user of the PIM application to
• Find relevant information faster
• With greater coverage

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Knowledge transfer/management

• Problem The same information is gathered and
  reviewed by hundreds and perhaps thousands of
  people, often by people in the same research
  laboratory
• Approach Developing a client-server system that
• Gives users access to information assembled by
  others with similar needs
• Integrates this access into the PIM application.

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PIM Application with Text Mining
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Knowledge Management System
Ankyrins
Ankyrins KA
Ankyrins
Ankyrins
Ankyrins
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More specifically
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The text-mining system highlights passages of
potential interest as documents are viewed.
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Researcher uses text-mining tools to extract
useful structured information
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Text Mining
Protein Pmid
6 IkappaBepsilon 94005619
5 Pp40 1533932
gt1 P85 11399775
gt1 Swi6 9521763
5 MAD-3 1891714
gt1 p16INK4a 7780957
gt1 p15INK4b 7780957
gt1 p16C 10843863

• prot has-repeats count

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Text Mining

• prot interacts-with prot
• prot inhibits prot
• prot ltfunctiongt organella

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Researcher selects highlighted passages/structured
data he finds useful.
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He manually highlights any other passages he
finds useful
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He also highlights a few useful figures.
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and selects some web-based information to be
saved.
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Knowledge Artifact
Protein Pmid
6 IkappaBepsilon 94005619
5 Pp40 1533932
gt1 P85 11399775
gt1 Swi6 9521763
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Instead of doing the digging again
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Others can reuse this
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More Information

• http//dollar.biz.uiowa.edu/sbradsha/Machete
• shannon-bradshaw_at_uiowa.edu