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Interpretable Deep Learning

This Genome Biology special issue is guest edited by Peter Koo of Cold Spring Harbor Laboratory, Sara Mostafavi of the University of Washington, and Asa Ben-Hur of Colorado State University.

Deep learning models have demonstrated a powerful ability to accurately model various genomics data. However, their impact on biology depends on the ability to interpret the models and discover new findings. We would like to invite submissions that focus on this aspect of deep learning:  new methods for making interpretable predictions for genomics data or studies that demonstrate the ability of these architectures to make novel biological discoveries. 

Genome Biology highlights timely advances in interpretable deep learning with applications in genomics.


  1. In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons...

    Authors: Junru Jin, Yingying Yu, Ruheng Wang, Xin Zeng, Chao Pang, Yi Jiang, Zhongshen Li, Yutong Dai, Ran Su, Quan Zou, Kenta Nakai and Leyi Wei
    Citation: Genome Biology 2022 23:219