Paper ID: 2306.05143

Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window Transformer

Zehui Li, Akashaditya Das, William A V Beardall, Yiren Zhao, Guy-Bart Stan

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.

Submitted: Jun 8, 2023