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. 2023 Jan 26;41(8):1117–1129. doi: 10.1038/s41587-022-01624-4

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 4 — a, Training and validation accuracy during pretraining step. b, Training and validation accuracy during fine-tuning step. c, Helix sequence reconstruction accuracy with different numbers of masked residues. d, Comparison of differences between predicted and real selection logos using the developed model and ZFPred based on the mean-square error (MSE) of predicted position weight matricies (PWMs) to ground-truth PWMs. e, Comparison of differences between predicted and real selection logos using the two-helix model and concatenated logos from the single-helix design model. f, Comparison of differences between predicted and real selection logos using the two-helix model and concatenated logos from single-helix B1H selections. g, Predicted logos, real B1H logos and concatenated single-helix B1H logos for test set sequences.