The role of visual analytics in asthma phenotyping and biomarker discovery

Suresh K. Bhavnani, Justin Drake, Rohit Divekar

Research output: Chapter in Book/Report/Conference proceedingChapter

8 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAdvances in Experimental Medicine and Biology
PublisherSpringer New York LLC
Pages289-305
Number of pages17
Volume795
ISBN (Print)9781461486022
DOIs
StatePublished - 2014

Publication series

NameAdvances in Experimental Medicine and Biology
Volume795
ISSN (Print)00652598

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Keywords

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

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Bhavnani, S. K., Drake, J., & Divekar, R. (2014). The role of visual analytics in asthma phenotyping and biomarker discovery. In Advances in Experimental Medicine and Biology (Vol. 795, pp. 289-305). (Advances in Experimental Medicine and Biology; Vol. 795). Springer New York LLC. https://doi.org/10.1007/978-1-4614-8603-9-18, https://doi.org/10.1007/978-1-4614-8603-9_18