The Role of Visual Analytics in Asthma Phenotyping and Biomarker Discovery

Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 795)

Abstract

The exponential growth of biomedical data related to diseases such as asthma far exceeds our cognitive abilities to comprehend it for tasks such as biomarker discovery, pathway identification, and molecular-based phenotyping. This chapter discusses the cognitive and task-based reasons for why methods from visual analytics can help in analyzing such large and complex asthma data, and demonstrates how one such approach called network visualization and analysis can be used to reveal important translational insights related to asthma. The demonstration of the method helps to identify the strengths and limitations of network analysis, in addition to areas for future research that can enhance the use of networks to analyze vast and complex biomedical datasets related to diseases such as asthma.

Keywords

Asthma Phenotypes Visual analytics Network analysis Visualization Bipartite networks Multivariate analysis Exploratory visual analysis Quantitative verification Emergent clusters Inference of biological pathways Molecular-based classification Phenotyping Biomarker discovery 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Suresh K. Bhavnani
    • 1
  • Justin Drake
    • 1
  • Rohit Divekar
    • 2
  1. 1.Institute for Translational SciencesUniversity of Texas Medical BranchGalvestonUSA
  2. 2.Division of Allergy and ImmunologyUniversity of Texas Medical BranchGalvestonUSA

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