The language of genetic code embodies a complex grammar and rich syntax of interacting molecular elements. Recent advances in self-supervision and feature learning suggest that statistical learning techniques can identify high-quality quantitative representations from inherent semantic structure. We present a gene-based language model that generates whole-genome vector representations from a population of 16 disease-causing bacterial species by leveraging natural contrastive characteristics between individuals. To achieve this, we developed a set-based learning objective, AB learning, that compares the annotated gene content of two population subsets for use in optimization. Using this foundational objective, we trained a Transformer model to backpropagate information into dense genome vector representations. The resulting bacterial representations, or embeddings, captured important population structure characteristics, like delineations across serotypes and host specificity preferences. Their vector quantities encoded the relevant functional information necessary to achieve state-of-the-art genomic supervised prediction accuracy in 11 out of 12 antibiotic resistance phenotypes.

No description available

No description available