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Strategies for Molecular Classification of Asthma Using Bipartite Network Analysis of Cytokine Expression

  • IMMUNE DEFICIENCY AND DYSREGULATION (DP HUSTON, SECTION EDITOR)
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Abstract

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.

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Acknowledgments

The authors acknowledge support from the UTMB CTSA grant. Drs. Pillai and Divekar contributed equally to this study.

Disclosure

Dr. Brasier has received grant support from the National Institutes of Health. Dr. Calhoun has received grant and travel support from the NHLBI/Asthma Clinical Research Network. Drs. Pillai, Divekar, and Bhavnani reported no potential conflicts of interest relevant to this article.

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Authors and Affiliations

  1. Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Texas Medical Branch, 4.118 John Sealy Annex, 301 University Blvd, Galveston, TX, 77555-0568, USA

    William J. Calhoun

  2. Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Texas Medical Branch, 5.140 John Sealy Annex, 301 University Blvd, Galveston, TX, 77555-0561, USA

    Regina R. Pillai

  3. Department of Internal Medicine, Division of Allergy and Clinical Immunology, University of Texas Medical Branch, 5.140 John Sealy Annex, 301 University Blvd, Galveston, TX, 77555-0561, USA

    Rohit Divekar

  4. Institute for Translational Sciences, University of Texas Medical Branch, MRB 8.122, 301 University Blvd, Galveston, TX, 77555-1060, USA

    Allan Brasier

  5. Institute for Translational Sciences, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555-0129, USA

    Suresh Bhavnani

Authors
  1. Regina R. Pillai
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  2. Rohit Divekar
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  3. Allan Brasier
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  4. Suresh Bhavnani
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  5. William J. Calhoun
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Corresponding author

Correspondence to William J. Calhoun.

Additional information

Regina R. Pillai and Rohit Divekar contributed equally to this work.

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Pillai, R.R., Divekar, R., Brasier, A. et al. Strategies for Molecular Classification of Asthma Using Bipartite Network Analysis of Cytokine Expression. Curr Allergy Asthma Rep 12, 388–395 (2012). https://doi.org/10.1007/s11882-012-0279-y

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