  Leveraging the vast genetic diversity within microbiomes offers unparalleled
insights into complex phenotypes, yet the task of accurately predicting and
understanding such traits from genomic data remains challenging. We propose a
framework taking advantage of existing large models for gene vectorization to
predict habitat specificity from entire microbial genome sequences. Based on
our model, we develop attribution techniques to elucidate gene interaction
effects that drive microbial adaptation to diverse environments. We train and
validate our approach on a large dataset of high quality microbiome genomes
from different habitats. We not only demonstrate solid predictive performance,
but also how sequence-level information of entire genomes allows us to identify
gene associations underlying complex phenotypes. Our attribution recovers known
important interaction networks and proposes new candidates for experimental
follow up.
