Title: PETRA: Pretrained Evolutionary Transformer for SARS-CoV-2 Mutation Prediction
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Generative Pretrained Transformers, Phylogenetics, Mutation Prediction, Computational Biology, SARS-CoV-2 Prediction

Abstract: Since its emergence, SARS-CoV-2 has demonstrated a rapid and unpredictable evolutionary trajectory, characterized by the continual emergence of immune-evasive variants. This poses persistent challenges to public health and vaccine development. 

 While large-scale generative pre-trained transformers (GPTs) have revolutionized the modeling of sequential data, their direct applications to noisy viral genomic sequences are limited. In this paper, we introduce PETRA(Pretrained Evolutionary TRAnsformer), a novel transformer approach based on evolutionary trajectories derived from phylogenetic trees rather than raw RNA sequences. This method effectively mitigates sequencing noise and captures the hierarchical structure of viral evolution. 

With a weighted training framework to address substantial geographical and temporal imbalances in global sequence data, PETRA excels in predicting future SARS-CoV-2 mutations, achieving a weighted recall@1 of 9.45% for nucleotide mutations and 17.10% for spike amino-acid mutations, compared to 0.49% and 6.64% respectively for the best baseline. PETRA also demonstrates its ability to aid in the real-time mutation prediction of major clades like 24F(XEC) and 25A(LP.8.1).

TL;DR: A transformer pretrained on evolutionary trajectory aids real-world SARS-CoV-2 mutation prediction.