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.
Curr Allergy Asthma Rep. 2012 Oct;12(5):388-95. doi: 10.1007/s11882-012-0279-y.
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.
哮喘是一种气道的慢性炎症性疾病，可导致各种程度的反复发作的呼吸症状，影响全球的患者。哮喘的某些特定亚组患者疾病严重，导致医疗保健费用增加和社会经济负担加重。尽管哮喘的患病率极高，但在预测治疗反应和识别有更高发病风险的患者方面仍存在局限性。该综合征表现出常见的临床体征和症状；然而，近年来人们已经意识到哮喘患者具有不同特征的亚组。研究人员试图描述哮喘的表型，最终帮助进行诊断和治疗应用。哮喘表型的方法有很多种；然而，它可以分为 2 个基本组，临床表型和分子表型。双相网络分析和可视化分析等创新技术为识别潜在的机制途径引入了数据分析的新维度。
