Cell–cell interactions orchestrate organismal development, homeostasis and single-cell
functions. When cells do not properly interact or improperly decode molecular messages …

Background Machine learning (ML) is a growing field in medicine. This narrative review
describes the current body of literature on ML for clinical decision support in infectious …

The use of vaccines have resulted in a remarkable improvement in global health. It has
saved several lives, reduced treatment costs and raised the quality of animal and human …

Ticks infest a variety of animal species and transmit pathogens causing disease in both
humans and animals worldwide. Tick–host–pathogen interactions have evolved through …

Pathogens produce effectors to overcome plant immunity, thereby threatening crop yields
and global food security. Large-scale interactomic studies have revealed that pathogens …

Motivation Can we predict protein–protein interactions (PPIs) of a novel virus with its host?
Three major problems arise: the lack of known PPIs for that virus to learn from, the cost of …

In the post-genomic era, proteomics has achieved significant theoretical and practical
advances with the development of high-throughput technologies. Especially the rapid …

Host-microbiome interactions play significant roles in human health and disease. Artificial
intelligence approaches have been developed to better understand and predict the …

In recent years, massive attention has been attracted to the development and application of
machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for …

Hundreds of different species colonize multicellular organisms making them
“metaorganisms”. A growing body of data supports the role of microbiota in health and in …