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An automated framework for efficiently designing deep convolutional neural networks in genomics

A preprint version of the article is available at bioRxiv.


Convolutional neural networks (CNNs) have become a standard for analysis of biological sequences. Tuning of network architectures is essential for a CNN’s performance, yet it requires substantial knowledge of machine learning and commitment of time and effort. This process thus imposes a major barrier to broad and effective application of modern deep learning in genomics. Here we present Automated Modelling for Biological Evidence-based Research (AMBER), a fully automated framework to efficiently design and apply CNNs for genomic sequences. AMBER designs optimal models for user-specified biological questions through the state-of-the-art neural architecture search (NAS). We applied AMBER to the task of modelling genomic regulatory features and demonstrated that the predictions of the AMBER-designed model are significantly more accurate than the equivalent baseline non-NAS models and match or even exceed published expert-designed models. Interpretation of AMBER architecture search revealed its design principles of utilizing the full space of computational operations for accurately modelling genomic sequences. Furthermore, we illustrated the use of AMBER to accurately discover functional genomic variants in allele-specific binding and disease heritability enrichment. AMBER provides an efficient automated method for designing accurate deep learning models in genomics.

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Fig. 1: Method and workflow overview of AMBER.
Fig. 2: AMBER searched architectures outperform sampled architectures.
Fig. 3: Illustration of AMBER architecture search logistics.
Fig. 4: Benchmarking variant effect prediction with allele-specific binding.
Fig. 5: Benchmarking heritability enrichment in disease GWAS.

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Data availability

All data used in this study are publicly available and the URLs are provided in the corresponding sections in Methods. Training data for the genomic regulatory features were downloaded from as described in ref. 4. The ground-truth data for allele-specific binding analysis were obtained from the supplementary data of ref. 29. The UK Biobank GWAS summary statistics data are reported in ref. 40 and downloaded from

Code availability

The AMBER package is available on GitHub at; the analysis presented in this study is available on GitHub at The AMBER code is publicly available on Zenodo at


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We acknowledge all members of the Troyanskaya laboratory for helpful discussions. We acknowledge that the work in this paper was performed at the high-performance computing resources at Simons Foundation. O.G.T. is a CIFAR fellow.

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Authors and Affiliations



Z.Z. and O.G.T. conceived the study. Z.Z. implemented the experiments. C.Y.P. and C.L.T. contributed research materials and analytic tools. Z.Z. and O.G.T. wrote the paper.

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Correspondence to Olga G. Troyanskaya.

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The authors declare no competing interests.

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Peer review information Nature Machine Intelligence thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Figs. 1–6.

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Supplementary Tables 1–3.

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Zhang, Z., Park, C.Y., Theesfeld, C.L. et al. An automated framework for efficiently designing deep convolutional neural networks in genomics. Nat Mach Intell 3, 392–400 (2021).

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