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. 2012 Oct;12(5):388-95.
doi: 10.1007/s11882-012-0279-y.

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

Regina R Pillai  1 , Rohit Divekar, Allan Brasier, Suresh Bhavnani, William J Calhoun
Affiliations

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

Regina R Pillai et al. Curr Allergy Asthma Rep. 2012 Oct.

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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Conflict of interest statement

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.

Figures

Fig. 1
Fig. 1
Network basics. A simple network is composed of nodes and edges. Here the network attempts to describe the relationship between the drug and clinical reaction. Red nodes denote the drug. The white nodes denote the reaction. The edges connect the nodes. White nodes (reaction) are distributed in space according to their degree of association to the red nodes (chemical agent). By this representation subsets of reactions and drugs can be identified. For example, it appears that DDT, Hepatachor, and Dieldrin can lead to similar side effects
Fig. 2
Fig. 2
Network analysis of asthma patients and cytokines. The network reveals cytokine and patient clusters
See this image and copyright information in PMC

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