Carrillo Barrera, P.; Babey, A.; Pena, C. A.

The scalability of phage therapy as a viable alternative or complement to antibiotics is limited by the labor-intensive experimental screening required to identify compatible phage-bacterium pairs. To accelerate this discovery process, we propose FoundedPBI, an ensemble deep learning approach that leverages the emergent capabilities of genomic foundation models, large language models pre-trained on vast DNA corpuses to predict phage-bacterium interactions from DNA sequences alone. We employ an ensemble strategy that aggregates outputs from three state-of-the-art DNA language models into a unified meta-embedding, which is then processed by a neural classifier. Our approach makes two key contributions: (1) We demonstrate that performing ensemble learning across models trained on different genomic data i.e., prokaryotic (Nucleotide Transformer v2, DNABERT-2) and bacteriophage (MegaDNA) genomes, captures partially-orthogonal biological signals, yielding 6% F1-score improvement over the best individual model. (2) We adapt long-context NLP aggregation strategies to handle whole bacterial and phage genomes (up to 5M base pairs) that exceed the foundation models context windows (12-96K bp) by a factor of 50-100, a critical challenge largely unaddressed in prior genomic deep learning work. On the PredPHI benchmark, FoundedPBI achieves a 76% F1-score outperforming the current state-of-the-art (PBIP) by 7%. On our internal dataset (CI4CB), we achieve 93% F1-score, improving our previous best methods by 4%. These results demonstrate that ensemble learning with proper long-context handling enables effective knowledge transfer of genomic foundation models to specialized prediction tasks.