简编
稳健性(进化)
标杆管理
计算生物学
生物
主机响应
传染病(医学专业)
生物信息学
疾病
免疫学
遗传学
医学
基因
免疫系统
业务
营销
考古
病理
历史
作者
Daniel G. Chawla,Antonio Cappuccio,Andrea Tamminga,Stuart C. Sealfon,Elena Zaslavsky,Steven H. Kleinstein
出处
期刊:Cell systems
[Elsevier]
日期:2022-12-01
卷期号:13 (12): 974-988.e7
被引量:4
标识
DOI:10.1016/j.cels.2022.11.007
摘要
Identification of host transcriptional response signatures has emerged as a new paradigm for infection diagnosis. For clinical applications, signatures must robustly detect the pathogen of interest without cross-reacting with unintended conditions. To evaluate the performance of infectious disease signatures, we developed a framework that includes a compendium of 17,105 transcriptional profiles capturing infectious and non-infectious conditions and a standardized methodology to assess robustness and cross-reactivity. Applied to 30 published signatures of infection, the analysis showed that signatures were generally robust in detecting viral and bacterial infections in independent data. Asymptomatic and chronic infections were also detectable, albeit with decreased performance. However, many signatures were cross-reactive with unintended infections and aging. In general, we found robustness and cross-reactivity to be conflicting objectives, and we identified signature properties associated with this trade-off. The data compendium and evaluation framework developed here provide a foundation for the development of signatures for clinical application. A record of this paper's transparent peer review process is included in the supplemental information.
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