测序与机器学习携手对抗新出现的病原体：预览
Sequencing meets machine learning to fight emerging pathogens: A preview.
作者信息
Yakimovich Artur
机构信息
Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR), Görlitz 02826, Germany.
Bladder Infection and Immunity Group (BIIG), Department of Renal Medicine, Division of Medicine, University College London, Royal Free Hospital Campus, London NW3 2PF, United Kingdom.
出版信息
Patterns (N Y). 2022 Feb 11;3(2):100448. doi: 10.1016/j.patter.2022.100448.
Abstract
In searching for SARS-CoV variants-of-concern, pathogen sequencing is generating an impressive amount of data. However, beyond epidemiological use, these data contain cues fundamental to our understanding of pathogen evolution in the human population. Yet, to harness them, further development of computational methodology, such as machine learning, may be required. This preview discusses updates in machine learning to understand emerging pathogens.
摘要
在寻找严重急性呼吸综合征冠状病毒（SARS-CoV）的关注变异株时，病原体测序正在产生海量数据。然而，除了用于流行病学研究外，这些数据还包含了对于我们理解病原体在人群中进化至关重要的线索。然而，要利用这些数据，可能需要进一步发展机器学习等计算方法。本综述讨论了机器学习在理解新出现病原体方面的进展。
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