Emerging applications of artificial intelligence in pathogen genomics
Carl J. E. Suster, David Pham, Jen Kok, Vitali Sintchenko
Abstract
The analysis of microbial genomes has long been recognised as a complex and data-rich domain where artificial intelligence (AI) can assist. As AI technologies have matured and expanded, pathogen genomics has also contended with exponentially larger datasets and an expanding role in clinical and public health practice. In this mini-review, we discuss examples of emerging applications of AI to address challenges in pathogen genomics for precision medicine and public health. These include models for genotyping whole genome sequences, identifying novel pathogens in metagenomic next generation sequencing, modelling genomic information using approaches from computational linguistics, phylodynamic estimation, and using large language models to make bioinformatics more accessible to non-experts. We also examine factors affecting the adoption of AI into routine laboratory and public health practice and the need for a renewed vision for the potential of AI to assist pathogen genomics practice.