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Call for Papers: Interpretable Deep Learning Special Issue

Guest editors: Peter Koo, Sara Mostafavi, and Asa Ben-Hur

Genome Biology is excited to announce a Call for Papers for a new special issue on Interpretable Deep Learning

The special issue, which is planned for Fall 2022, will be 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. With a special issue, Genome Biology will highlight timely advances in interpretable deep learning with applications in genomics.

The special issue will accept Research, Method/Software, Short Report and Database manuscript submissions presenting outstanding contributions that highlight new findings in the field of interpretable deep learning for genomics, including (but not limited to):

- Genetic studies of phenotype

- Regulatory genomics

- Modeling gene expression, including single cell data

- Novel deep learning architectures for genomics data

- Explainable AI

Submission deadline: 01 May 2022

Please use the online submission system, and indicate in your cover letter that you would like the manuscript to be considered for the ‘Interpretable Deep Learning’ special issue. If you would like to inquire about the suitability of a manuscript for consideration, please email a pre-submission inquiry to

Kevin Pang 
Special Issue Editor, Genome Biology

Barbara Cheifet
Chief Editor, Genome Biology