Predicting Influenza Reassortment Potential Using Foundation Models and Genetic Algorithms for Pandemic Preparedness

Seeraja Konnur

Keywords: Reassortment, Influenza, Foundation Model, Genetic Algorithm

TL;DR: Predicting influenza A virus reassortment potential using DNABERT2 and genetic algorithms from circulating environmental samples

Abstract:

Influenza A virus (IAV) poses a persistent global
threat due to its rapid evolution through reassort-
ment, which hampers vaccine design, antiviral
development, and causes recurring outbreaks. We
present a novel computational framework that
combines DNABERT-2, a foundation model for
genomic sequences, with genetic algorithms to
predict reassortment events. Using environmen-
tal surveillance data, our method identifies both
known and hypothetical reassortants and scores
them based on biological plausibility. Dimension-
ality reduction reveals clear separation between
reassortant and non-reassortant embeddings. This
enables early detection of high-risk strains, of-
fering a scalable tool for pandemic preparedness.
Our approach supports proactive strategies for
vaccine, therapeutic, and economic resilience.

Submission Number: 90