2022
DOI: 10.1016/j.isci.2021.103617
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A gradient tree boosting and network propagation derived pan-cancer survival network of the tumor microenvironment

Abstract: Predicting cancer survival from molecular data is an important aspect of biomedical research because it allows quantifying patient risks and thus individualizing therapy. We introduce XGBoost tree ensemble learning to predict survival from transcriptome data of 8,024 patients from 25 different cancer types and show highly competitive performance with state-of-the-art methods. To further improve plausibility of the machine learning approach we conducted two additional steps. In the first step, we applied pan-ca… Show more

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Cited by 5 publications
(22 citation statements)
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“…Download and documentation is available from https://git-scm.com/ . CRITICAL: The indicated software and package versions were used in ( Thedinga and Herwig, 2022 ). Other versions than the ones indicated here were not tested.…”
Section: Before You Beginmentioning
confidence: 99%
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“…Download and documentation is available from https://git-scm.com/ . CRITICAL: The indicated software and package versions were used in ( Thedinga and Herwig, 2022 ). Other versions than the ones indicated here were not tested.…”
Section: Before You Beginmentioning
confidence: 99%
“…For these patients, only the sample with the lexicographically highest TCGA barcode is kept. Note: For the analyses in ( Thedinga and Herwig, 2022 ) and shown in the Figures of this protocol, TCGA data was downloaded using manifest files and the GDC Data Transfer Tool. This can lead to a different ordering of samples as compared to downloading the data with TCGAbiolinks, which results in different training and test data splits when training the XGBoost survival prediction model.…”
Section: Before You Beginmentioning
confidence: 99%
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