Yakimovich Artur
Artificial Intelligence for Life Sciences CIC, London, UK.
Infect Drug Resist. 2021 Aug 20;14:3319-3326. doi: 10.2147/IDR.S292743. eCollection 2021.
The research of interactions between the pathogens and their hosts is key for understanding the biology of infection. Commencing on the level of individual molecules, these interactions define the behavior of infectious agents and the outcomes they elicit. Discovery of host-pathogen interactions (HPIs) conventionally involves a stepwise laborious research process. Yet, amid the global pandemic the urge for rapid discovery acceleration through the novel computational methodologies has become ever so poignant. This review explores the challenges of HPI discovery and investigates the efforts currently undertaken to apply the latest machine learning (ML) and artificial intelligence (AI) methodologies to this field. This includes applications to molecular and genetic data, as well as image and language data. Furthermore, a number of breakthroughs, obstacles, along with prospects of AI for host-pathogen interactions (HPI), are discussed.
病原体与其宿主之间相互作用的研究是理解感染生物学的关键。从单个分子层面开始，这些相互作用决定了传染因子的行为及其引发的结果。传统上，宿主-病原体相互作用（HPI）的发现涉及一个逐步费力的研究过程。然而，在全球大流行期间，通过新颖的计算方法加速快速发现的需求变得愈发迫切。本综述探讨了HPI发现的挑战，并研究了目前为将最新的机器学习（ML）和人工智能（AI）方法应用于该领域所做的努力。这包括对分子和遗传数据以及图像和语言数据的应用。此外，还讨论了人工智能在宿主-病原体相互作用（HPI）方面的一些突破、障碍以及前景。
