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 2008Pacific 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 to display to users term expansions

5. 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 Design Prototype Evaluate

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 Synonyms4.42 Gene’s Synonyms refined by organism4.02 Gene’s Homologs3.75 Genes from same family: parents3.47 Genes from same family: children 3.64 Genes from same family: siblings 3.29 Genes this gene interacts with3.74 Diseases this gene is associated with 3.46 Chemicals/drugs this gene is associated with 3.2 8 Localization information for this gene 3.73 1 2 3 45 (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 DesignParticipants who rated design 1st or 2nd Average rating (1=low, 4=high) #% 3 (checkboxes) 15793.3 4 (categories) 10532.6 2 (links) 9472.5 1 (baseline) 001.6

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 chose either individual names or whole groups with one click. Split in preference between the system suggesting only names that it is 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: Text searchFigure caption search Table search Frequently1176 Sometimes453 Rarely034 Never002 Undecided111

21. questions after the designs

22. 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

25. 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