  Given the increasing volume and quality of genomics data, extracting new
insights requires interpretable machine-learning models. This work presents
Genomic Interpreter: a novel architecture for genomic assay prediction. This
model outperforms the state-of-the-art models for genomic assay prediction
tasks. Our model can identify hierarchical dependencies in genomic sites. This
is achieved through the integration of 1D-Swin, a novel Transformer-based block
designed by us for modelling long-range hierarchical data. Evaluated on a
dataset containing 38,171 DNA segments of 17K base pairs, Genomic Interpreter
demonstrates superior performance in chromatin accessibility and gene
expression prediction and unmasks the underlying `syntax' of gene regulation.
