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Network analysis of clinical trials on depression: implications for comparative effectiveness research.抑郁症临床试验的网络分析:对比较效果研究的启示
AMIA Annu Symp Proc. 2010 Nov 13;2010:51-5.
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The nested structure of cancer symptoms. Implications for analyzing co-occurrence and managing symptoms.癌症症状的嵌套结构。对分析症状共现和管理症状的启示。
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Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations.通过三维网络可视化发现肾脏疾病与调控基因之间的隐藏关系。
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Uniform definition of asthma severity, control, and exacerbations: document presented for the World Health Organization Consultation on Severe Asthma.哮喘严重程度、控制和恶化的统一定义:世界卫生组织严重哮喘咨询会议提交的文件。
J Allergy Clin Immunol. 2010 Nov;126(5):926-38. doi: 10.1016/j.jaci.2010.07.019.
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Predicting intermediate phenotypes in asthma using bronchoalveolar lavage-derived cytokines.利用支气管肺泡灌洗液中的细胞因子预测哮喘的中间表型。
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Analyses of asthma severity phenotypes and inflammatory proteins in subjects stratified by sputum granulocytes.根据痰中粒细胞对哮喘严重程度表型和炎症蛋白进行分析。
J Allergy Clin Immunol. 2010 May;125(5):1028-1036.e13. doi: 10.1016/j.jaci.2010.02.008. Epub 2010 Apr 15.
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Identification of asthma phenotypes using cluster analysis in the Severe Asthma Research Program.利用严重哮喘研究计划中的聚类分析鉴定哮喘表型。
Am J Respir Crit Care Med. 2010 Feb 15;181(4):315-23. doi: 10.1164/rccm.200906-0896OC. Epub 2009 Nov 5.
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Circos: an information aesthetic for comparative genomics.Circos:一种用于比较基因组学的信息美学。
Genome Res. 2009 Sep;19(9):1639-45. doi: 10.1101/gr.092759.109. Epub 2009 Jun 18.
9
T-helper type 2-driven inflammation defines major subphenotypes of asthma.2型辅助性T细胞驱动的炎症反应定义了哮喘的主要亚表型。
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Network analysis of toxic chemicals and symptoms: implications for designing first-responder systems.有毒化学物质与症状的网络分析:对设计应急响应系统的启示
AMIA Annu Symp Proc. 2007 Oct 11;2007:51-5.

细胞因子在哮喘患者中的共同出现方式:从二分网络分析到基于分子的分类。

How cytokines co-occur across asthma patients: from bipartite network analysis to a molecular-based classification.

机构信息

Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, United States; Preventive Medicine & Community Health, University of Texas Medical Branch, Galveston, TX, United States; School of Biomedical Informatics, University of Texas, Houston, TX, United States.

Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, United States.

出版信息

J Biomed Inform. 2011 Dec;44 Suppl 1(Suppl 1):S24-S30. doi: 10.1016/j.jbi.2011.09.006. Epub 2011 Oct 1.

DOI:10.1016/j.jbi.2011.09.006
PMID:21986291
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3277832/
Abstract

Asthmatic patients are currently classified as either severe or non-severe based primarily on their response to glucocorticoids. However, because this classification is based on a post-hoc assessment of treatment response, it does not inform the rational staging of disease or therapy. Recent studies in other diseases suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. We therefore measured cytokine values in bronchoalveolar lavage (BAL) samples of the lower respiratory tract obtained from 83 asthma patients, and used bipartite network visualizations with associated quantitative measures to conduct an exploratory analysis of the co-occurrence of cytokines across patients. The analysis helped to identify three clusters of patients which had a complex but understandable interaction with three clusters of cytokines, leading to insights for a state-based classification of asthma patients. Furthermore, while the patient clusters were significantly different based on key pulmonary functions, they appeared to have no significant relationship to the current classification of asthma patients. These results suggest the need to define a molecular-based classification of asthma patients, which could improve the diagnosis and treatment of this disease.

摘要

哮喘患者目前主要根据其对糖皮质激素的反应分为严重或非严重型。然而,由于这种分类是基于治疗反应的事后评估,它不能为疾病的合理分期或治疗提供信息。最近在其他疾病中的研究表明,包含分子信息的分类方法可能导致更准确的诊断和治疗反应预测。因此,我们测量了 83 名哮喘患者下呼吸道支气管肺泡灌洗液 (BAL) 样本中的细胞因子值,并使用二分网络可视化及其相关定量测量方法对患者之间细胞因子的共现进行了探索性分析。该分析有助于识别出三组具有复杂但可理解的相互作用的患者,以及三组细胞因子,为哮喘患者的基于状态的分类提供了新的思路。此外,虽然患者聚类基于关键肺功能显著不同,但它们似乎与哮喘患者的当前分类没有显著关系。这些结果表明需要定义一种基于分子的哮喘患者分类方法,这可能会改善这种疾病的诊断和治疗。

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