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Scientists' sense making when hypothesizing about disease mechanisms from expression data and their needs for
1School of Education, University of Michigan, Ann Arbor, Michigan 48109, USA. bmirel@umich.edu.
BMC Bioinformatics
|April 29, 2014
Summary
Scientists analyze high-throughput expression data to form disease hypotheses. This study observed one scientist to model the sense-making process, informing better bioinformatics tool design for expression-driven, omics hypothesizing.
Area of Science:
- Bioinformatics and Computational Biology
- Genomics and Molecular Biology
- Data Visualization and Visual Analytics
Background:
- Expression-driven, omics hypothesizing involves analyzing high-throughput experimental data to uncover functional relationships and generate disease mechanism hypotheses.
- Scientists currently employ interactive data visualizations and extensive literature review in this analytical process.
- The end-to-end reasoning and sense-making behaviors of scientists during this analysis are not well understood, hindering the development of truly supportive bioinformatics tools.
Discussion:
- This research presents a preliminary sense-making model derived from an in-depth, six-month observational study of a single scientist performing expression-driven, omics hypothesizing.
- The study characterizes the stages of the scientist's sense-making process and provides derived guidelines for developing effective visualization tools.
- While acknowledging the limitations of a single case study, the findings offer a foundation for further research into analytical workflows in bioinformatics.
Key Insights:
- A preliminary sense-making model for expression-driven, omics hypothesizing has been abstracted from observational data.
- Guidelines for designing visualization tools that better support scientists' analytical workflows were developed.
- Understanding the scientist's sense-making process is crucial for creating more effective bioinformatics tools.
Outlook:
- Further research is needed to generalize the findings beyond the single case study and validate the proposed sense-making model.
- The developed guidelines can inform the design of next-generation bioinformatics visualization tools tailored to scientists' analytical needs.
- This work aims to stimulate increased interest and investigation into scientists' analytical workflows and their impact on bioinformatics tool development.
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