1. Machete: Charting Excursions through Bioscience Literature Shannon Bradshaw 1 and Marc Light 2 1 Department of Management Sciences 2 School of Library and Information Science 2 Department of Linguistics 1,2 Department of Computer Science The University of Iowa

2. 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

3. 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

4. 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

5. 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

6. 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.

7. PIM Application with Text Mining

8. Knowledge Management System “Ankyrins” “Ankyrins” KA “Ankyrins”

9. More specifically…

12. The text-mining system highlights passages of potential interest as documents are viewed.

14. Researcher uses text-mining tools to extract useful structured information

15. Text Mining Info Extraction prot has-repeats count #ProteinPmid 6IkappaBepsilon94005619 5Pp401533932 >1P8511399775 >1Swi69521763 5MAD-31891714 >1p16INK4a7780957 >1p15INK4b7780957 >1p16C10843863

16. Text Mining Info Extraction prot interacts-with prot prot inhibits prot prot organella

17. Researcher selects highlighted passages/structured data he finds useful.

18. He manually highlights any other passages he finds useful…

20. He also highlights a few useful figures.

22. …and selects some web-based information to be saved.

24. Knowledge Artifact #ProteinPmid 6IkappaBepsilon94005619 5Pp401533932 >1P8511399775 >1Swi69521763

26. Instead of doing the digging again

27. Others can reuse this

28. More Information http://dollar.biz.uiowa.edu/~sbradsha/Machete shannon-bradshaw@uiowa.edu