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. 2023 Feb 17;13:2861. doi: 10.1038/s41598-023-30124-x

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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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Runtime comparison between RNAfold with or without RNA-par in different ranges of RNA length. The dataset used was TS’ (See Table 1). The unit of measurement for runtime is second. The time of loading deep learning libraries was not taken into account. Our model was built under the framework of keras. In our server, the model of GPU was NVIDIA GeForce RTX 2080 Ti, the model of CPU was Intel(R) Xeon(R) Platinum 8164. Only one GPU and CPU were used. The capacity of the memory was 128 G.