Loguercio Salvatore, Calverley Ben C, Wang Chao, Shak Daniel, Zhao Pei, Sun Shuhong, Budinger G R Scott, Balch William E
Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA.
Division of Pulmonary and Critical Care Medicine, Northwestern University, Chicago, IL, USA.
Patterns (N Y). 2023 Jul 21;4(8):100800. doi: 10.1016/j.patter.2023.100800. eCollection 2023 Aug 11.
We have developed a machine learning (ML) approach using Gaussian process (GP)-based spatial covariance (SCV) to track the impact of spatial-temporal mutational events driving host-pathogen balance in biology. We show how SCV can be applied to understanding the response of evolving covariant relationships linking the variant pattern of virus spread to pathology for the entire SARS-CoV-2 genome on a daily basis. We show that GP-based SCV relationships in conjunction with genome-wide co-occurrence analysis provides an early warning anomaly detection (EWAD) system for the emergence of variants of concern (VOCs). EWAD can anticipate changes in the pattern of performance of spread and pathology weeks in advance, identifying signatures destined to become VOCs. GP-based analyses of variation across entire viral genomes can be used to monitor micro and macro features responsible for host-pathogen balance. The versatility of GP-based SCV defines starting point for understanding nature's evolutionary path to complexity through natural selection.
我们开发了一种基于高斯过程（GP）的空间协方差（SCV）的机器学习（ML）方法，以追踪时空突变事件对生物学中宿主-病原体平衡的影响。我们展示了如何将SCV应用于每日理解整个SARS-CoV-2基因组中病毒传播变异模式与病理学之间不断演变的协变关系的响应。我们表明，基于GP的SCV关系与全基因组共现分析相结合，为关注变体（VOC）的出现提供了一个早期预警异常检测（EWAD）系统。EWAD可以提前数周预测传播和病理学表现模式的变化，识别注定会成为VOC的特征。基于GP对整个病毒基因组变异的分析可用于监测负责宿主-病原体平衡的微观和宏观特征。基于GP的SCV的多功能性为理解自然通过自然选择走向复杂性的进化路径定义了起点。
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