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. 2021 Dec 6;11:23452. doi: 10.1038/s41598-021-02282-3

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© The Author(s) 2021

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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A schematic representation of the study design. This study was aimed at predicting novel drug targets for DN based on the holistic molecular pathogenesis map. Using different experimental and computational methods, the central nodes, key interactions, and signaling pathways of DN were identified. To translate the findings to clinical application, a high-performance machine learning framework, mGMDH-AFS, was developed and validated to predict drug targets for all human proteins. This classifier was then applied to candidate novel therapeutic targets in the constructed holistic map of DN. miRs: microRNAs; PPI: protein–protein interaction network.