Strategies for molecular classification of asthma using bipartite network analysis of cytokine expression

Regina R. Pillai, Rohit Divekar, Allan Brasier, Suresh Bhavnani, William J. Calhoun

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Asthma is a chronic inflammatory disease of the airways that leads to various degrees of recurrent respiratory symptoms affecting patients globally. Specific subgroups of asthma patients have severe disease leading to increased healthcare costs and socioeconomic burden. Despite the overwhelming prevalence of the asthma, there are limitations in predicting response to therapy and identifying patients who are at increased risk of morbidity. This syndrome presents with common clinical signs and symptoms; however, awareness of subgroups of asthma patients with distinct characteristics has surfaced in recent years. Investigators attempt to describe the phenotypes of asthma to ultimately assist with diagnostic and therapeutic applications.Approaches to asthma phenotyping are multifold; however, it can be partitioned into 2 essential groups, clinical phenotyping and molecular phenotyping. Innovative techniques such as bipartite network analysis and visual analytics introduce a new dimension of data analysis to identify underlying mechanistic pathways.

Original languageEnglish (US)
Pages (from-to)388-395
Number of pages8
JournalCurrent Allergy and Asthma Reports
Issue number5
StatePublished - Oct 2012


  • Airway
  • Allergic
  • Asthma
  • Bipartite
  • Exercise induced
  • Heterogeneous
  • Inflammation
  • Logistic regression
  • Molecular
  • Multivariate adaptive regression splines (MARS) Cytokines Cluster analysis Classification regression trees (CART)
  • Network
  • Phenotype

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology
  • Pulmonary and Respiratory Medicine


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