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. 2019 Nov 15;9:16932. doi: 10.1038/s41598-019-53324-w

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© The Author(s) 2019

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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Results of the randomization experiments. This figure shows the results of the experiments on the 3 tasks tested in this study (Cysteine oxidation (CYS), Relative Solvent Accessibility (RSA) and Secondary Structure (SS) predictions). The plots show the performances obtained by 4 ML methods (Multilayer Perceptron (MLP), Random Forest (RF), Ridge classifier (Ridge) and Linear Support Vector Machine (LinSVC)) in terms of their AUC (MCC in the SS case). For each ML method and each task we ran five simulations, testing the scores obtained with different features encoding. For each combination of task and ML method, we tested the ONEHOT encoding (ONEHOT), randomly sampled propensity scales (REAL), randomly shuffled propensity scales (SHUFFLED), randomly generated scales (RANDGEN) and a true randomization of the vectors (see Methods for more details).