Host-Microbe Interactions: Prospects of Machine Learning and Deep Learning Technologies in Animal Viral Disease Management

Simple Summary Viral diseases in farm animals spread quickly and can severely damage animal health and food production. At the heart of these infections is a complex "host-microorganism interaction": viruses must enter animal cells, use the host's machinery to multiply, and at the same time avoid being eliminated by the immune system. Understanding these interactions in a systematic way is difficult with traditional experiments alone because they are slow, expensive, and capture only part of this complexity. This review explains how modern computer methods, especially machine learning and deep learning, can help scientists study host-virus interactions more efficiently. We describe how these tools can analyze large sets of genetic and protein data to identify which animal molecules viruses use to enter cells and which host pathways they disrupt. We also summarize how computer models can support early detection of infection on farms and assist in selecting drug and vaccine targets that interfere with key host-virus interactions. Although challenges remain, such as limited data quality and rapidly changing viruses, this approach offers promising new ways to protect animal health and strengthen food security.Abstract The rapid industrialization of global livestock production has intensified the threat of viral epidemics, in which the intestinal, respiratory, and reproductive systems are susceptible to viral attacks. Understanding the mechanism of virus-host interactions will facilitate the development of prevention strategies against highly mutable and fast-spreading pathogens. This review examines recent progress in applying machine learning (ML) and deep learning (DL) to the study and control of animal viral diseases. By analyzing existing research, we show how these techniques improve the prediction of host-microbe interactions, support continuous monitoring of animal health, and accelerate the discovery of drug targets and vaccine candidates. Integrating ML and DL frameworks enables more accurate modeling of complex biological processes and offers new tools for data-driven veterinary science. Nevertheless, challenges remain, including unbalanced datasets, the structural and evolutionary complexity of viruses, and the poor cross-species transferability of predictive models. Future work should emphasize algorithmic designs suited to small-sample, multivariate time series data and promote the development of intelligent systems that unite virology, immunology, and epidemiology. The combination of reinforcement learning for optimizing vaccination strategies and unsupervised learning for detecting emerging pathogens may ultimately lead to adaptive, efficient, and precise systems for disease prevention, supporting both animal health and sustainable livestock development.

Journal

VETERINARY SCIENCES

Year

2025

Volume

12

Issue

12

eISSN

2306-7381

DOI

10.3390/vetsci12121129

WoS UID

WOS:001647467900001