Fig. 1From: Identifying common transcriptome signatures of cancer by interpreting deep learning modelsA Upset plot summarizing pairwise differential gene expression analyses performed on tumors and their corresponding normal tissue. No gene is significantly deregulated in more than 9 out of 11 cancer types tested. B, C Dataset assembled to train and test binary models to distinguish between normal and tumor samples shown by tissue type (B) or dataset (C). D Graphical representation of the computational framework used to train, test, and interpret the models. E Performance of models trained with protein-coding gene expression, lncRNA gene expression, or splicing variations evaluated by area under the precision-recall curve (AUPRC) and accuracy (sum of true positives and true negatives over the total population). F Accuracy of models trained with protein-coding gene expression, lncRNA gene expression, or splice junction usage across the 13 datasets used to assemble the training set. G Performance of models trained with protein-coding gene expression, lncRNA gene expression, or splice junction usage on an independent dataset consisting of normal and cancer lung samples. H Performance of the deep learning model, SVM, and random forest using protein-coding gene expression on unseen tissue types (blood cancers) with no batch correction. The training set consists of solid tumors onlyBack to article page