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. 2023 Sep 29;20:41. doi: 10.1186/s12014-023-09426-9

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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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 3 — Classification and Segregation potential of selected features using Machine Learning. (3A) Schematic outline of Machine Learning and Data analysis workflow. (3B and C) illustrates the top-ranked features using Gini and Gain ratio in regards to Low Grade and High-Grade segregation in tissue and Serum samples, (3D and E) Model optimization for serum samples using ROC Curve for Tissue and Serum markers; (3F) shows the clustering plot for the combination of MUC4 and MUC1 followed by SPTB2 and S100A11; (3G) shows the clustering plot for the combination of TF and FN1 followed by TF and APOB; (3H and I) represent the confusion matrix using top features for tissue and serum respectively