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. 2024 Jul 24;14:17064. doi: 10.1038/s41598-024-67023-8

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

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Comparison of model evaluation processes. (A) Test metric distributions obtained with bootstrap (n = 1000 per seed) on the test sets generated by different seeds on the downstream tasks. Models were trained following our repeated holdout pipeline, but graphs show results only on arbitrary chosen individual test sets to showcase the limitations of this evaluation process. p-values are computed using Wilcoxon tests. (i) Comparison of representation models on 3 example cohorts from the per-cohort OS prediction task. (ii) Comparison of AE and scVI on the pan-cancer task. (iii) Comparison of MAE and DA-GN on the Gene Essentiality task. (B) Overall comparison of the different representation models over the tasks Per-cohort OS Prediction, Pan-cohort OS prediction and gene essentiality (overall metric and per-DepOI). The score for a given model corresponds to the number of winning pairwise comparisons to other models according to our acceptance test criteria. The sum is then normalized by task to obtain the final score and is expressed as a percentage.