Identification of asthma phenotypes using cluster analysis in the Severe Asthma Research Program.
By W. Moore, D. Meyers, S. Wenzel, W. G. Teague, Huashi Li, Xingnan Li, R. D'Agostino, M. Castro, Douglas Curran-Everett, Anne M. Fitzpatrick and 12 more. American Journal of Respiratory and Critical Care Medicine, 2010
2,017 citations recorded by the source corpus.
Subfields: Physiology, Pulmonary and Respiratory Medicine, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/65614eddb7a1dbed7385a21df6d294327ee8d9e0
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Citations in the corpus
- Meta-analysis of Genome-wide Association Studies of Asthma In Ethnically Diverse North American Populations (2011, methodology): “6 (5-17) NA Freiburg Chicago 459 302:157 NA NA 9.”
- Innate lymphoid cells and asthma. (2014, neutral): “Obesity, which is associated with type 2 diabetes mellitus, cardiovascular disease, liver disease, and some forms of cancer,(68) is also a major risk factor for the development of asthma,(69-71) particularly for a severe, therapy-resistant form of asthma distinct from allergic asthma.(72) In a mouse”
- International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma (2013, methodology): “Chronic airflow limitation which is less responsive to bronchodilators and inhaled or oral CS therapy is observed in some severe asthma phenotypes [9, 11, 26].”
- Finding and Leveraging Structure in Learning Problems (2012, background): “Data matrices with signals that are both sparse and highly structured form a natural model for several real-world activations such as when we have a group of genes (belonging to a common pathway for instance) co-expressed under the influence of a set of similar drugs [208], or when we have groups of”