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. 2023 Apr 26;13:6821. doi: 10.1038/s41598-023-32293-1

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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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An AI-based approach for processing and interpreting scRNAseq datasets. (A) The scRNAseq analytical pipeline is displayed as a flowchart with the data processing steps in arrows and the input and output data for each step displayed as cylinders. (B) The architecture of the AI Autoencoder is displayed as a schematic.