PDF(Pubmed)
Abstract：
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.
摘要：
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。从单个分子的水平开始，这些相互作用定义了传染因子的行为及其引发的结果。宿主-病原体相互作用(HPI)的发现通常涉及逐步费力的研究过程。然而，在全球大流行中，通过新的计算方法快速加速发现的冲动变得如此强烈。这篇综述探讨了HPI发现的挑战，并调查了当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的应用，以及图像和语言数据。此外，一些突破,障碍,随着人工智能对宿主-病原体相互作用(HPI)的前景，正在讨论。
