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. 2021 Jun 29;11:13505. doi: 10.1038/s41598-021-92799-4

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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/.

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Visualization of feature representations learned by MultiSurv. We collected the internal fused feature representation vector of the MultiSurv model trained on clinical and mRNA data and embedded it into a two-dimensional space using t-SNE. (a) Embedded feature representations for each patient in the test dataset. Patients diagnosed with each of four selected cancer types are highlighted. Within each of the highlighted cancer types, visually selected outlier patients are annotated. All patient survival curves, highlighting the selected outliers, are displayed for (b) PRAD, (c) KIRC, (d) GBM, and (e) OV. PRAD prostate adenocarcinoma, KIRC kidney renal clear cell carcinoma, OV ovarian serous cystadenocarcinoma, GBM glioblastoma multiforme.