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HRMAn 2.0: Next-generation artificial intelligence–driven analysis for broad host–pathogen interactions
Cellular Microbiology ( IF 2.6 ) Pub Date : 2021-04-30 , DOI: 10.1111/cmi.13349
Daniel Fisch 1, 2 , Robert Evans 1, 2 , Barbara Clough 1 , Sophie K Byrne 1 , Will M Channell 1 , Jacob Dockterman 3 , Eva-Maria Frickel 1
Affiliation  

To study the dynamics of infection processes, it is common to manually enumerate imaging-based infection assays. However, manual counting of events from imaging data is biased, error-prone and a laborious task. We recently presented HRMAn (Host Response to Microbe Analysis), an automated image analysis program using state-of-the-art machine learning and artificial intelligence algorithms to analyse pathogen growth and host defence behaviour. With HRMAn, we can quantify intracellular infection by pathogens such as Toxoplasma gondii and Salmonella in a variety of cell types in an unbiased and highly reproducible manner, measuring multiple parameters including pathogen growth, pathogen killing and activation of host cell defences. Since HRMAn is based on the KNIME Analytics platform, it can easily be adapted to work with other pathogens and produce more readouts from quantitative imaging data. Here we showcase improvements to HRMAn resulting in the release of HRMAn 2.0 and new applications of HRMAn 2.0 for the analysis of host–pathogen interactions using the established pathogen T. gondii and further extend it for use with the bacterial pathogen Chlamydia trachomatis and the fungal pathogen Cryptococcus neoformans.

中文翻译:

HRMAn 2.0:下一代人工智能驱动的广泛宿主-病原体相互作用分析

为了研究感染过程的动态,通常手动枚举基于成像的感染测定。然而,根据成像数据对事件进行手动计数是有偏差的、容易出错并且是一项艰巨的任务。我们最近推出了 HRMAn(宿主对微生物分析的响应),这是一种自动图像分析程序,使用最先进的机器学习和人工智能算法来分析病原体生长和宿主防御行为。借助HRMAn,我们可以以公正且高度可重复的方式量化多种细胞类型中弓形虫和沙门氏菌等病原体的细胞内感染,测量包括病原体生长、病原体杀灭和宿主细胞防御激活在内的多个参数由于 HRMAn 基于 KNIME 分析平台,因此它可以轻松适应其他病原体,并从定量成像数据中生成更多读数。在这里,我们展示了 HRMAn 的改进,导致 HRMAn 2.0 的发布,以及 HRMAn 2.0 的新应用,用于使用已确定的病原体弓形虫分析宿主-病原体相互作用,并进一步扩展其用于细菌病原体沙眼衣原体真菌病原体新型隐球菌
更新日期:2021-06-23
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