Gradient tree boosting and network propagation for the identification of pan-cancer survival networks

Kristina Thedinga; Ralf Herwig

doi:10.1016/j.xpro.2022.101353

Gradient tree boosting and network propagation for the identification of pan-cancer survival networks

STAR Protoc. 2022 Apr 23;3(2):101353. doi: 10.1016/j.xpro.2022.101353. eCollection 2022 Jun 17.

Authors

Kristina Thedinga¹, Ralf Herwig¹

Affiliation

¹ Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany.

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).

Keywords: Bioinformatics; Cancer; Genomics; Health Sciences; RNAseq; Systems biology.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Machine Learning*
Neoplasms* / genetics
Protein Interaction Maps
Transcriptome