Item

Abstract

Cancer survival prediction is typically done with uninterpretable machine
learning techniques, e.g., gradient tree boosting. Therefore, additional steps
are needed to infer biological plausibility of the predictions. Here, we describe
a protocol that combines pan-cancer survival prediction with XGBoost tree-
ensemble learning and subsequent propagation of the learned feature weights
on protein interaction networks. This protocol is based on TCGA transcriptome
data of 8,024 patients from 25 cancer types but can easily be adapted to cancer
patient data from other sources.
For complete details on the use and execution of this protocol, please refer to
Thedinga and Herwig (2022).