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Evidence for Showing Gene/Protein Name Suggestions in Bioscience Literature Search Interfaces

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  1. Evidence for Showing Gene/Protein Name Suggestions in Bioscience Literature Search Interfaces Anna Divoli, Marti A. Hearst, Michael A. Wooldridge School of Information University of California, Berkeley Supported by NSF DBI-0317510 08 Jan 2008 Pacific Symposium of Biocomputing

  2. outline • BioText search engine (in brief) • Aims • HCI principles (in brief) • First study: biological information preferences • Second study: gene/protein name expansion preferences • Conclusions from studies • Current and future work

  3. biotext search engine

  4. aims • Determine whether or not bioscience literature searchers wish to see related term suggestions, in particular, gene and protein names • Determine how todisplay to users term expansions

  5. Design Evaluate Prototype hci principles • Design for the user, not for the designers or the system • Needs assessment: who users are what their goals are what tasks they need to perform • Task analysis: characterize what steps users need to take create scenarios of actual use decide which users and tasks to support • Iterate between: designing & evaluating

  6. hci principles - cont. • Make use of cognitive principles where available • Important guidelines: Reduce memory load Speak the user’s language Provide helpful feedback Respect perceptual principles • Prototypes: Get feedback on the design faster Experiment with alternative designs Fix problems before code is written Keep the design centered on the user

  7. first study: biological information preferences • Online survey • Questions on what they are searching for in the literature and what information would like a system to suggest • 38 participants: • - 7 research institutions • - 22 graduate students, 6 postdocts, 5 faculty, and 5 others • - wide range of specialties: systems biology, bioinformatics,genomics, biochemistry, cellular and evolutionary biology, microbiology, physiology, ecology...

  8. participants’ information

  9. results Related Information Type Avg rating # selecting 1 or 2 Gene’s Synonyms 4.4 2 Gene’s Synonyms refined by organism 4.0 2 Gene’s Homologs 3.7 5 Genes from same family: parents 3.4 7 Genes from same family: children 3.6 4 Genes from same family: siblings 3.2 9 Genes this gene interacts with 3.7 4 Diseases this gene is associated with 3.4 6 Chemicals/drugs this gene is associated with 3.2 8 Localization information for this gene 3.7 3 1 2345 (Do NOT want this) (Neutral) (REALLY want this)

  10. second study: gene/protein name expansion preferences • Online survey • Evaluating 4 designs for gene/protein name suggestions • 19 participants: • 9 of which also participated in the first study • 4 graduate students, 7 postdocs, 3 faculty, and 5 others • wide range of specialties: molecular toxicology, evolutionary genomics,chromosome biology, plant reproductive biology, cell signaling networks, computational biology…

  11. design 1: baseline

  12. design 2: links

  13. design 3: checkboxes

  14. design 4: categories

  15. results

  16. conclusions • Strong desire for the search system to suggest information closely related to gene/protein names. • Some interest in less closely related information . • All participants want to see organism names in conjunction with gene names. • A majority of participants prefer to see term suggestions grouped by type (synonyms, homologs, etc). • Split in preference between single-click hyperlink interaction (categories or single terms) and checkbox-style interaction. • The majority of participants prefers to have the option to choseeither individual names or whole groups with one click. • Split in preference between the system suggesting only names that itis highly confident are related and include names that it is less confident about under a “show more” link.

  17. in progress: biotext’s name suggestions http://bebop.berkeley.edu/biotext-dev/

  18. current / future work • Evaluation of the different views of BioText search engine. • We plan to assess presentation of other results of text analysis, such as the entities corresponding to diseases, pathways, gene interactions, localization information, function information, and so on. • Assess the usability of one feature at a time, see how participants respond, and then test out other features • Need to experiment with hybrid designs, e.g., checkboxes for the individual terms and a link that immediately adds all terms in the group and executes the query. • Adding more information will require a delicate balancing act between usefulness and clutter!

  19. acknowledgments • We are grateful to all the participants of our studies! • BioText is funded by NSF DBI-0317510 • Travel support by PSB/NIH • BioText Search Engine available at: http://biosearch.berkeley.edu

  20. current study • Evaluating the different views of BioText search engine • 16 participants (so far): • - 6 graduate students, 4 postdocs, 1 faculty, 5 other • Results:

  21. questions after the designs

  22. questions after the designs Other: 1:“Not sure if prefer mouse-over or showing organism” 2:“But it should be easy to access the other info”

  23. questions after the designs

  24. questions after the designs

  25. questions after the designs Other: 1: “Allow user to specify” 2: “let user search (wide)false pos v neg hits as pref”

  26. more information • First usability study: • Hearst, M.A., Divoli, A., Wooldridge, M., and Ye, J. “Exploring the Efficacy of Caption Search for Bioscience Journal Search Interfaces”, BioNLP Workshop at ACL 2007, Prague, Czech Republic • The BioText Search Engine: • Hearst, M.A., Divoli, A., Guturu, H., Ksikes, A., Nakov, P., Wooldridge, M. and Ye, J. (2007) “BioText Search Engine: beyond abstract search”, Bioinformatics, 23: 2196-2197