A-B.03: Explainable Deep Learning for Phage-Host Infection Prediction from Whole Genomes
Authors
Institute for Information and Communication Technologies (IICT), School of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland
Carlos Pena-Reyes
HEIG-VD
Arthur Babey
Institute for Information and Communication Technologies (IICT), School of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland
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Deep learning models are increasingly applied to predict bacteriophage-host infections from whole genomes, yet their decision-making processes remain opaque. This lack of interpretability hinders model validation and limits the usefulness of their predictions for downstream biological applications. To address this challenge, we present a reproducible Explainable Artificial Intelligence (XAI) pipeline designed to translate raw deep learning signals into interpretable genomic insights. Our framework combines complementary attribution methods to identify important genomic regions, which are then automatically mapped to functional annotations via a bioinformatics workflow. We illustrate the utility of this approach across four different bacterial hosts. The results show that the reference deep learning model consistently prioritises biologically relevant functional modules, such as tail structural proteins and lysis machinery, rather than relying on noise. Furthermore, while these high-importance regions are predominantly unique, they functionally converge on conserved viral mechanisms. By transforming abstract importance scores into domain-specific biological summaries, this work provides a transparent framework that facilitates interpreting and validating deep learning models
in environmental genomics.
Co-authors: Federico García-Valenzuela, Arthur Babey
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