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. 2018 Jan 26;9(2):65. doi: 10.3390/genes9020065

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© 2018 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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Visualization of breast cancer subtypes using selected 29 non-coding (a) and 36 “all” (b) gene features for 839 TCGA RNAseq training set; the sensitivity and specificity of prediction accuracy based on ROC curve for 29 non-coding (c) and 36 “all” (d) gene features.

Visualization of breast cancer subtypes using selected 29 non-coding (a) and 36 “all” (b) gene features for 839 TCGA RNAseq training set; the sensitivity and specificity of prediction accuracy based on ROC curve for 29 non-coding (c) and 36 “all” (d) gene features.