Machine Learning and Artificial Intelligence for the Prediction of Host–Pathogen Interactions: A Viral Case

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.

Keywords:

• virus
• deep learning
• virus-cell interaction
• sequence analysis
• computer vision
• natural language processing
• NLP
• DL

Abbreviations

ACE-2, angiotensin-converting enzyme 2; AI, artificial intelligence; AUC, area under receiver operating characteristics curve; COVID-19, coronavirus disease 2019; CORD-19, COVID-19 open research data set; CT, computed tomography; DL, deep learning; DNN, deep artificial neural networks; EM, electron microscopy; GLUE, General Language Understanding Evaluation; HCoV, human coronaviruses; HPI, host–pathogen interactions; KD, knowledge discovery; ML, machine learning; NLP, natural language processing; NER, named entity recognition; SARS-CoV2, severe acute respiratory syndrome coronavirus 2; STS, semantic text similarity; TEND, transformer query-target knowledge discovery; QA, question answering.

Disclosure

The author reports no conflicts of interest in this work.