1 / 20

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

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. 08 Jan 2008 Pacific Symposium of Biocomputing. outline.

whitleyj
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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 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 to display 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 surveys • Questions on they are searching 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 surveys • 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 . • Most 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 • (link to development site) • (take screenshots when the interface is ready as a fall-back plan)

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

  20. acknowledgments • We are grateful to all the participants of our studies! • Supported by NSF DBI-0317510 • BioText Search Engine available at: http://biosearch.berkeley.edu

More Related