Science Daily July 21, 2023
An international team of researchers (USA – Scripps Research Institute, China) developed a machine learning approach using Gaussian process-based spatial covariance (SCV) to track the impact of spatial-temporal mutational events driving host-pathogen balance in biology. They showed how SCV could 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. The GP-based SCV relationships in conjunction with genome-wide co-occurrence analysis provided an early warning anomaly detection (EWAD) system for the emergence of variants of concern (VOCs). EWAD could anticipate changes in the pattern of performance of spread and pathology weeks in advance, identifying signatures destined to become VOCs. According to the researchers 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… read more. Open Access TECHNICAL ARTICLEÂ
Graphical BSTRACT. Credit: Patterns, July 21, 2023Â