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. 2018 Sep;24(9):1119–1132. doi: 10.1261/rna.062802.117

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© 2018 Johnson et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society

This article is distributed exclusively by the RNA Society for the first 12 months after the full-issue publication date (see http://rnajournal.cshlp.org/site/misc/terms.xhtml). After 12 months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

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FIGURE 3. — Heat map representation of the difference between Isoform and Gene f-measure across machine learning methods, classes, data sets, and normalization techniques. For the majority of classification tasks, using isoform-level rather than gene-level expression data resulted in a small to substantial increase of the performance accuracy, represented by f-measure values here. The bottom x-axis represents the machine learning techniques ([DT] Decision Table, [J48] J48 Decision Tree, [LR] Linear Regression, [NB] Naïve Bayes, [RF] Random Forest, [SVM] Support Vector Machine). The y-axis represents the classes considered. MC stands for multiclass. The top x-axis represents normalization techniques including Nothing (no normalization), Standardized, and Normalized. Data sets for each panel are (A) RBM, (B) NCBI, (C) TCGA–log₂ normalized counts, and (D) TCGA–raw counts.