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. 2018 Sep 4;8:13202. doi: 10.1038/s41598-018-31497-0

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© The Author(s) 2018

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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The pipeline to evaluate the predictive performance of RRSF. (A) Data preparation. Dataset 1 was randomly split into a training set (50%) and a test set (50%). The training set was used to train the RRSF model, while the test set and independent datasets were used to evaluate its predictive performance. (B) Topological weights of genes were inferred using DRW. (C) Selection process for genes with best predictive performance. The number of genes were narrowed down by several iterative steps, in which, according to their VIMPs, genes ranking in the lowest 10% in terms of importance were discarded at each step. (D) Development of predictive models for all combinations of the 10 genes. Biomarkers with the highest C-index were identified. (E) Evaluation of predictive performance by C-index, Kaplan–Meier curves, and log-rank tests.