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. 2021 Mar 6;22:110. doi: 10.1186/s12859-021-04049-z

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© The Author(s) 2021

Open AccessThis 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 — Rule-based model evaluation for the autism-control dataset performed with the Johnson reduction method. Discretization levels were obtained from the equal frequency method by categorizing the features into three bins. a ROC AUC for the model. Sensitivity that is as a true positive rate (TPR) and 1-specificity that is a false positive rate (FPR). b VisuNet network of co-predictive features for the autism class. Connection values represent the strength of node or edge. These values were estimated based on the rule support and accuracy. Rules were selected based on their statistical significance (Bonferroni-adjusted P ≤ 0.05). c Distribution of the significance of rules in the model. Bonferroni-adjusted P values were marked as ns(P > 0.05), *(P ≤ 0.05), **(P ≤ 0.01) and ***(P ≤ 0.001). d Distribution of support sets for the top-ranked rule from the recalculated model. Support sets represent sets of objects that fulfil the RHS of the rule (THEN-part). Boxplots display scaled gene expression values for objects supporting and non-supporting the given rule