Supporting cognition in systems biology analysis: findings on users' processes and design implications

Barbara Mirel

doi:10.1186/1747-5333-4-2

Abstract

BackgroundCurrent usability studies of bioinformatics tools suggest that tools for exploratory analysis support some tasks related to finding relationships of interest but not the deep causal insights necessary for formulating plausible and credible hypotheses. To better understand design requirements for gaining these causal insights in systems biology analyses a longitudinal field study of 15 biomedical researchers was conducted. Researchers interacted with the same protein-protein interaction tools to discover possible disease mechanisms for further experimentation.ResultsFindings reveal patterns in scientists' exploratory and explanatory analysis and reveal that tools positively supported a number of well-structured query and analysis tasks. But for several of scientists' more complex, higher order ways of knowing and reasoning the tools did not offer adequate support. Results show that for a better fit with scientists' cognition for exploratory analysis systems biology tools need to better match scientists' processes for validating, for making a transition from classification to model-based reasoning, and for engaging in causal mental modelling.ConclusionAs the next great frontier in bioinformatics usability, tool designs for exploratory systems biology analysis need to move beyond the successes already achieved in supporting formulaic query and analysis tasks and now reduce current mismatches with several of scientists' higher order analytical practices. The implications of results for tool designs are discussed.

Highlights

Current usability studies of bioinformatics tools suggest that tools for exploratory analysis support some tasks related to finding relationships of interest but not the deep causal insights necessary for formulating plausible and credible hypotheses
As usability studies show, initial query and analysis tasks that bioinformatics tools make possible for scientists are not coupled with the support scientists need for generating deep insights for hypothesizing
Overview of common research and analysis practices Fifteen scientists researched distinct problems related to different diseases and putative mechanisms. Regardless of these differences, all 15 of the scientists commonly started with candidate genes from prior experiments and queried the Michigan Molecular Integrating (MiMI) database for results on gene attributes and gene product interactions

Summary

Introduction

Current usability studies of bioinformatics tools suggest that tools for exploratory analysis support some tasks related to finding relationships of interest but not the deep causal insights necessary for formulating plausible and credible hypotheses. To better understand design requirements for gaining these causal insights in systems biology analyses a longitudinal field study of 15 biomedical researchers was conducted. To design for a better fit, Cohen and Hersh argue, bioinformatics specialists need to conduct more naturalistic investigations of biomedical researchers at work on real world analyses. To fill this need, I conducted field studies of 15 biomedical researchers as they conducted their actual systems biology analysis and aimed to formulate some hypothesis about mechanisms of a complex disease. All the scientists interacted (page number not for citation purposes)

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Discussion

Conclusion