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.
@article{arxiv.2306.05143,
title = {Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window Transformer},
author = {Zehui Li and Akashaditya Das and William A V Beardall and Yiren Zhao and Guy-Bart Stan},
journal= {arXiv preprint arXiv:2306.05143},
year = {2023}
}
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40th International Conference on Machine Learning (ICML 2023) Workshop on Computational Biology (WCB)