PHI-Reason: evidence-grounded species-level phage-host prediction from structured biological text profiles
Zhang, Y.-z.; Xu, L.; Imoto, S.
Show abstract
Phage-host interaction (PHI) prediction is a fundamental problem in microbiology. Existing approaches typically encode phage and host information as numerical representations derived from sequence similarity, protein content or reference databases, and use them to score candidate hosts or train prediction models. Although effective, this design obscures which biological evidence supports a prediction. Here, we present PHI-Reason, a species-level PHI prediction framework that reformulates host prediction as inference over structured biological text. PHI-Reason converts heterogeneous genomic, functional and contextual evidence into modular natural-language profiles, which a frozen large language model integrates at inference time for candidate-host ranking or pairwise PHI assessment. Across species-level benchmarks, PHI-Reason achieved competitive performance and complementary correct assignments relative to established methods. Its explicit profile design enables evidence perturbation and rationale-grounding analyses, making prediction support and hallucination risk operationally measurable. PHI-Reason provides an interpretable evidence-integration approach and demonstrates how large language models can support evidence-grounded PHI prediction.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.