Evidence for Showing Gene/Protein Name Suggestions in Bioscience Literature Search Interfaces
description
Transcript of 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
Supported by NSF DBI-0317510
08 Jan 2008 Pacific Symposium of Biocomputing
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
biotext search engine
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
hci principles
• Design for the user, not for the designers or the system
• Needs assessment: who users arewhat their goals arewhat 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
PrototypeEvaluate
hci principles - cont.
• Make use of cognitive principles where available
Important guidelines: Reduce memory loadSpeak the user’s language
Provide helpful feedbackRespect perceptual
principles
• Prototypes: Get feedback on the design fasterExperiment with alternative designs
Fix problems before code is written Keep the design centered on the user
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...
participants’ information
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 2 3 4 5
(Do NOT want this) (Neutral) (REALLY want this)
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…
design 1: baseline
design 2: links
design 3: checkboxes
design 4: categories
results
Design Participants who rated design 1st or 2nd
Average rating
(1=low, 4=high)# %
3
(checkboxes)
15 79 3.3
4
(categories)
10 53 2.6
2
(links)
9 47 2.5
1
(baseline)
0 0 1.6
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.
in progress: biotext’s name suggestions
http://bebop.berkeley.edu/biotext-dev/
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!
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
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 search Figure caption search
Table search
Frequently 11 7 6
Sometimes 4 5 3
Rarely 0 3 4
Never 0 0 2
Undecided 1 1 1
questions after the designs
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”
questions after the designs
questions after the designs
questions after the designs
Other: 1: “Allow user to specify”
2: “let user search (wide)false pos v neg hits as pref”
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