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