Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning.
Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning.
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DOI：
10.1016/j.xcrm.2022.100857
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发表时间：
2022-12-20
影响因子：
14.3
通讯作者：
Wenzel, Sally E.
中科院分区：
文献类型：
--
作者：
Ray, Anuradha;Das, Jishnu;Wenzel, Sally E.
利用机器学习将高维分子数据与临床特征相结合，精准诊断和管理疾病，这是前所未有的机会。哮喘是一种复杂的异质性疾病，不能完全用2型(T2)免疫反应异常来解释。现有的和新兴的哮喘多组学数据集显示不同生物途径的失调，包括那些与T2机制有关的途径。虽然T2导向的生物制剂已经改变了许多患者的生活，但它们并未被证明对许多其他患者有效，尽管它们的生物标志物特征相似。因此，迫切需要缩小这一差距以了解哮喘的异质性，这可以通过利用和整合丰富的多组学哮喘数据集和相应的临床数据来实现。这篇文章提供了一个机器学习方法的概要，可以用来弥合预测性生物标记物和实际原因标志之间的差距，这些标志在临床试验中得到验证，最终建立真正的哮喘内型。哮喘是一种免疫调节的异质性疾病。Ray等人。建议使用机器学习方法将组学数据集与临床特征相结合，以获得可操作的信息，用于临床试验的体内验证。由此确定的因果信号将揭开真正的哮喘内型，为T2指导的通路以外的治疗铺平道路。
There is unprecedented opportunity to use machine learning to integrate high-dimensional molecular data with clinical characteristics to accurately diagnose and manage disease. Asthma is a complex and heterogeneous disease and cannot be solely explained by an aberrant type 2 (T2) immune response. Available and emerging multi-omics datasets of asthma show dysregulation of different biological pathways including those linked to T2 mechanisms. While T2-directed biologics have been life changing for many patients, they have not proven effective for many others despite similar biomarker profiles. Thus, there is a great need to close this gap to understand asthma heterogeneity, which can be achieved by harnessing and integrating the rich multi-omics asthma datasets and the corresponding clinical data. This article presents a compendium of machine learning approaches that can be utilized to bridge the gap between predictive biomarkers and actual causal signatures that are validated in clinical trials to ultimately establish true asthma endotypes. Asthma is an immune-mediated heterogeneous disease. Ray et al. suggest use of machine learning approaches to integrate omics datasets with clinical characteristics to derive actionable information for in vivo validation in clinical trials. Causal signatures thereby identified will unravel true asthma endotypes, paving the way for therapeutics beyond T2-directed pathways.
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免疫途径定义了疫苗介导的对SIV的保护的多种机制。
影响因子：
82.9
作者：
Ackerman ME;Das J;Pittala S;Broge T;Linde C;Suscovich TJ;Brown EP;Bradley T;Natarajan H;Lin S;Sassic JK;O'Keefe S;Mehta N;Goodman D;Sips M;Weiner JA;Tomaras GD;Haynes BF;Lauffenburger DA;Bailey-Kellogg C;Roederer M;Alter G
通讯作者：
Alter G
影响因子：
7.4
作者：
Bender A;Cortes-Ciriano I
通讯作者：
Cortes-Ciriano I
影响因子：
9.5
作者：
Boulesteix, Anne-Laure;Strimmer, Korbinian
通讯作者：
Strimmer, Korbinian
影响因子：
30.5
作者：
Altman, Matthew C.;Gill, Michelle A.;Jackson, Daniel J.
通讯作者：
Jackson, Daniel J.
影响因子：
6.7
作者：
Das J;Devadhasan A;Linde C;Broge T;Sassic J;Mangano M;O'Keefe S;Suscovich T;Streeck H;Irrinki A;Pohlmeyer C;Min-Oo G;Lin S;Weiner JA;Cihlar T;Ackerman ME;Julg B;Deeks S;Lauffenburger DA;Alter G
通讯作者：
Alter G